The book is the result of my two academic interests. On a professional level I have too often found that there is a lot of misleading information being dished out on the reasons behind some of the most high profile cyber attacks. Both the media and the so called security experts end up in a blame game without factual evidence or a clear understanding of what lies behind the obvious. My research focuses on proposing a model for Cyber Criminal Psychology & Profiling that incorporates multiple intelligence, Interviewing Techniques, Cyber Criminal Psychology, Cyber forensics and Offender Profiling. The traditional model of offender profiling does not incorporate the human side of the profiler nor the offender. A better profile of a Cyber-Criminal will help in speeding up the investigation process and ensuring better identification of the Cyber-Criminal.

On a personal level, especially after going through a traumatic cancer struggle, I have found that people around me are missing vital things in life. Some out of ignorance and some out of misinterpretation of facts.

The book is a collection of 31 articles, which took almost three years of constant effort. The book is split into five chapters, each representing a unique theme, each with multiple articles of interest. Chapter 1 focuses on Cyber Forensics, Chapter 2 on Profiling, Chapter 3 on Interview Techniques, Chapter 4 on Forensics Psychology and Chapter 5 on Multiple Intelligences. Although the chapters are in a certain order, each article can be read on its own in any order.

The one thing I learnt in preparing the articles is how valuable knowledge of the self and surroundings are in figuring out better solutions for oneself and in the workplace. I hope you enjoy reading these articles as much as I enjoyed writing them. I also hope you find them useful.





There has been an increased interest in multiple dimensions of intelligence. This paper seeks to link intelligence to Micro-Expression. More specifically with Emotional Intelligence (EQ), Cultural Intelligence (CQ), and People Intelligence (PQ).

Micro-Expressions are often expressed involuntarily by humans on their faces based on the emotions experienced. These brief involuntary facial expressions are often expressed by individuals in situations where they feel they will either gain or lose. People express these expressions when they consciously make attempts to conceal their feelings or think about their feelings (Freitas-Magalhães, 2012; Ekman, 2003). They are brief in duration and last between half a second to several seconds. These expressions can be labeled, spotted and expressed in the same way basic emotions such as anger, contempt, surprise, sadness, joy, and fear are expressed (Ekman, 1999). According to Ekman (1992), basic and universal emotions including shame, fear, disgust, anger, happiness, surprise, sadness and anxiety are expressed in these Micro-Expressions. Ekman (1999) expanded the list of negative and positive emotions including that are aroused when individuals are exposed to certain situations: embarrassment, shame, relief, pride, pleasure, contentment, content amusement, pride, guilt, and anxiety. Ekman (1999) revealed that universally, people tend to express similar emotions whenever they are exposed to situations that provoke such emotions.

Micro-Expressions are classified into three depending on how they are modified by situations: simulated expressions, neutralized expressions, and masked expressions. Simulated expressions are Micro-Expressions that are not accompanied by non-genuine emotions. They are expressed as brief flashes of an expression. Neutralized expressions occur following the suppression of a genuine expression with the face remaining neutral. The successful suppression of neutralized expression makes it difficult for another person to observe them (Ekman & Friesen, 2003). On the other hand, masked expressions occur when a falsified expression completely masks a genuine expression. People tend to hide, either consciously or subconsciously, masked expressions (Ekman, & Friesen, 2003).

It can be hard to explicitly pick up and understand involuntary facial expressions. Goleman (1995) believes that these expressions are recorded and recognized in the unconscious mind as implicit competence. Goleman (1995) further believes that individuals have the capacity to recognize their own Micro-Expressions and emotions of other people and to introspectively discriminate these emotions based on such feelings. In EQ, empathy and reporting are guided by an unconscious synchrony referred to as attunement (Goleman, 1995). According to Goleman (1995) attunement relies on non-verbal communication. Involuntary behavior may be elicited by facial expressions in a process referred to as looping. Research on motor mimicry has revealed that neurons often display facial expressions through muscles in the face. It is believed that this occurs when neurons pick up facial expressions, which are then communicated to motor neurons that control the way muscles are expressed in the face. This suggests that an individual who tries to remain neutral in his or her Micro-Expression can be provoked to produce a smile by another individual displaying a smile in his or her face (Goleman, 2006). These involuntary habits, emotions, and functions take place when amygdala hijacks the pre-frontal cortex thereby impairing the better judgment and rationality (Goleman, 1995). This demonstrates how sensory memory and involuntary behavior can be interpreted and executed by the bottom brain. This demonstrates the role played by Micro-Expressions in attunement. It also reveals how one can interpret Micro-Expressions. Micro-Expressions of a hidden emotion that is displayed on a person will tend to induce same emotions in a process that Goleman (2006) referred to as emotional contagion. Individuals will the ability to introspect these Micro-Expressions have high EQ. Such individuals are believed to have the ability to read accurately and interpret emotions.


Emotions are data for making decisions, for protecting us, for initiating action, and for understanding others and oneself. Emotional Quotient and Emotional Intelligence are often used interchangeably to refer to an individuals’ ability to understand and recognize their emotions and those of other people, and the ability of people to manage their relationships and behavior by utilizing this awareness. Since its introduction by John Mayer and Peter Salovey in 1990, EQ has become one of the most controversial and widely investigated constructs in psychology (Zeidner, Matthews, & Roberts, 2009). The development of measuring instruments that can reliably measure EQ has also been problematic (Conte, 2005). Among the many EQ theories, Mayer and Salovey (1997)’s ability-based model has the strongest empirical and theoretical basis. The strength of this model includes the objective nature of measuring EQ and the low redundancy between the traditional concept of intelligence (Intelligence Quotation [IQ]) and personality.

The Mayer-Salovey-Caruso Emotional Intelligence Test (MSCEIT) test is believed to be valid in predicting the effectiveness of interpersonal and social activities (Zeidner, Matthews, & Roberts, 2009). Mayer and Salovey’s (1997) model identifies four branches of EQ: emotional facilitation of thinking; appraisal, perception, the perception of emotion; employing emotional knowledge and analysis and understanding emotions; and reflective regulation of emotions. Each of these branches describes emotional abilities. Abilities constituting the four branches are vital in emotional deception detection.

It is suggested that Emotional Intelligence can facilitate the recognition of Micro-Expressions and the detection of a lie. It is believed that an individual with high EQ can read Micro-Expressions and interpret them and that this is an important part of reading people and understanding nonverbal behavior (Pazian, 2014).

Existing research have shown that by reading and interpreting universal Micro-Expressions, including anger, fear, contempt, surprise, happiness, sadness and disgust, one can detect whether someone is lying or telling the truth (Wojciechowski, Stolarski, & Matthews, 2014). Wojciechowski, Stolarski, and Matthews (2014) examined whether individuals with high EQ can effectively detect emotional liars. It was revealed that individuals that demonstrate superior emotional perceptionsare more adept at detecting deception through the identification of mismatch between verbal messages and facial messages. Wojciechowski, Stolarski, and Matthews (2014) identified two personal factors believed to predict such abilities: high EQ and female gender. The analysis of Face Decoding Test confirmed the correlation between superior face decoding and EQ. results also confirmed gender differences in EQ with females found to have higher EQ than males. Results also revealed that integration of cognitive and emotional cues are core attributes of EQ and these attributes make it possible for the individual with high EQ to detect deception.

Elsewhere, Mayer, DiPaolo, and Salovey (1990) consider an individual’s ability to identify emotions in other individuals as the core ability of EQ. According to Mayer, DiPaolo and Salovey (1990), this ability is necessary though not sufficient for unmasking emotional liars and detecting emotional leakages. Mayer, DiPaolo and Salovey (1990) argue that without an effective perception of another person’s emotions, an individual may not be able to effectively detect emotional deceit in another person. For Mayer and Salovey (1997) an individual with high EQ can discriminate between dishonest and honest expressions of feelings. As noted by Mayer and Salovey (1997), an individual with emotional skills should also have the ability to make use of emotion in directing attention to important information. This process, also referred to as emotional facilitation of thoughts, may be used to support and improve the basic emotional perception skills. It is also important to recognize that having emotional understanding abilities, including having the ability to recognize relations between emotions and words may help an individual in interpreting the meaning that is conveyed by emotions regarding interpersonal interactions, and in recognizing transitions among emotions (Mayer, & Salovey, 1997). This emotional reasoning process is particularly important in cases where an individual is required to combine the verbal expressions of an interlocutor with information emerging from the facial expressions of the interlocutor (Vrij & Mann, 2004).

Studies by Porter et al. (2011) and Elfenbein et al. (2010) examined EQ within the context of deception. Porter et al. (2011) found that individuals with a high ability to express and perceive emotions have the ability to convincingly feign emotions than other individuals. However, it was noted that individual with these abilities do not have the ability to prevent emotional leakage. Elfenbein et al. (2010) found similar results. However, Elfenbein et al. (2010) only measured emotion recognition ability but not the overall EQ. In another study, Baker, ten Brinke and Porter (2012) examined whether high EQ was a defining characteristic of a “detection wizard”. Results showed that total EQ score and discrimination of lies and truth were not related. However, the perception score was found to be negatively associated with the detection of deceptive targets. In this study, the experimental design was specific and involved engaging real-life videos of people who were emotionally pleading for the missing family members to return safely. Half of these people played a significant role in the murder (disappearance) of the missing ones. This study, therefore, considered liars and high-stakes emotional deceptions.

It has been suggested that there are gender differences in the cognitive-emotional processes. It is believed that females have higher EQ than males (Van Rooy, Alonso, & Viswevaran, 2005). It is argued that females are superior to males when it comes to the detection of deceptions in their romantic partners (McCornack & Parks, 1990). It is believed that this is because women are superior when it comes to reading facial expressions and other nonverbal cues than men. Women are also believed to be superior when it comes to experimental “mind-reading tasks”, including a feeling of an acquaintance and inferring the thoughts (Thomas & Fletcher, 2003). Women are also believed to be superior in perceptual sensitivity and to have subtle non-verbal affective signals (Donges, Kersting, & Suslow, 2012). They tend to have a keen interest in nonverbal cues (Hurd & Noller, 1988).


Cultural Intelligence (CQ) is an individual’s capability to effectively work, relate and interact with people in culturally diverse contexts. Individuals with high CQ have the capability to successfully achieve their objectives within culturally diverse context. Such people have CQ Drive, CQ knowledge, CQ Action and CQ Strategy (Van Dyne, Ang, Ng, Rockstuhl, Tan, & Koh, 2012). Cultural Intelligence (CQ) impacts the person’s ability to interact with different cultures in an effective manner. It enables an individual to work and relate effectively across culture (James, Lenartowicz, & Apud, 2006). This tool can help improve an individual’s performance in different cultural settings and identify meanings that could be misunderstood or lost in translation in non-verbal behavior.

Ekman (2003) sheds light on how individuals from different cultures react differently to similar events. Ekman (2003) identified emotional triggers that elicit emotions whenever an individual encounters different situations: universal triggers, unique triggers and other triggers. Universal triggers elicit similar emotional in all individuals regardless of culture or personality. On the other hand unique triggers elicit different emotions in people depending on how they were socialized (i.e., personality and culture). For example, individuals from certain culture may be irritated by people speaking loudly while people from other cultures find it acceptable. While some cultures fear oceans, others seek to explore them. In Ekman (2003)’s view these variances are the result of how individual were socialized. There are other triggers (e.g., post-traumatic stress) that are rooted in the individual’s unique experience and personality. They understand and appreciate remarkable differences in people who are from different cultures. Ekman (1992) confirmed that that people from different cultures universally express Micro-Expressions: fear, happiness, surprise, disgust, anger sadness, and contempt. It is further argued that individuals with high CQ have same social sensibilities while relating and interacting with individuals from diverse cultures who display different and unique emotions in ways that are not familiar with them.


People Intelligence (PQ) indicates the individual’s capacity to work and relate with other people. PQ has three aspects: self-management; openness to others; and interpersonal effectiveness. People with high PQ are known to work well with people. They create a shared meaning, inspire and motivate others to work together as a team in order to actualize reality. They are self-aware and know their weaknesses and strengths. They have the ability to use their strengths to address or compensate for their weaknesses.

The Big Five personality traits, or the Five Factor Model (FFM) is a well-known model that describes personality. The model was initially proposed by Tupes & Christal (1961) and later improved by Digman (1990). The five factors are Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism, also known as OCEAN. Openness is the curiosity to experience something new; Conscientiousness is the tendency to be either organized or careless; Extraversion explains whether the person is outgoing or socially reserved; Agreeableness describes friendliness or compassion against being detached; and Neuroticism is about being sensitive or nervous against being secure and confident.

It is believed that an individual’s PQ is determined by factors such as experience, skills, social network, knowledge and Emotional Intelligence (EQ) (Ekman, Friesen, & O’Sullivan, 1988). It is also argued that people with high EQ have high PQ and have the capability to discriminate genuine facial expressions to fake ones (Ekman, Friesen, & O’Sullivan, 1988). It has been suggested that Micro-Expressions can use used for authenticity judgment (i.e., genuine and fake smiles) (Skinner, & Mullen, 1993; Frank, Ekman, & Friesen, 1993; Schmidt, Bhattacharya, & Denlinger, 2009).


A review of related literature has found links between Micro-Expression and human intelligence in the form of EQ, CQ and PQ. Understanding this concept could help detecting lies and avoiding deception. Applications of this technique are in interview techniques and criminal investigation that could allow the investigator to catch the liars. Not able to identify subtle non-verbal behavior could have disastrous consequences.


1.Baker, A., ten Brinke, L., & Porter, S. (2012). Will get fooled again: Emotionally intelligent people are easily duped by high-stake deceivers. Leg. Crim. Psychol , 18, 300–313.

2.Conte, J. M. (2005). A review and critique of emotional intelligence measure. J Organ Behav , 26, 433–440.

3.Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology , 41, 417–440.

4.Donges, U., Kersting, A., & Suslow, T. (2012). Women’s Greater Ability to Perceive Happy Facial Emotion Automatically: Gender Differences in Affective Priming. PLoS One , 7, 1–5.

5.Ekman, P. (1999). Basic Emotions. In T. Dalgleish, & M. Power, Handbook of Cognition and Emotion. Sussex, UK: John Wiley & Sons, Ltd.

6.Ekman, P. (2003). Emotions Revealed. New York: Henry Holt and Co.

7.Ekman, P. (1992). Facial Expressions of Emotion: An Old Controversy and New Findings. London: Philosophical Transactions of the Royal Society.

8.Ekman, P., & Friesen, W. V. (2003). Unmasking the Face. Cambridge: Malor Books.

9.Ekman, P., Friesen, W. V., & O’Sullivan, M. (1988). Smiles when lying. J Pers Soc Psychol , 54, 414–420.

10.Elfenbein, H. A., Foo, M. D., Mandal, M., Biswal, R., Eisenkraft, N., Angeline, L., et al. (2010). Individual differences in the accuracy of expressing and perceiving nonverbal cues: New data on an old question. J Res Pers , 44, 199–206.

11.Frank, M. G., Ekman, P., & Friesen, W. V. (1993). Behavioral markers and recognizability of the smile of enjoyment. J Pers Soc Psychol , 64, 83–93.

12.Freitas-Magalhães, A. (2012). Microexpression and macroexpression. In V. S. Ramachandran, Encyclopedia of Human Behavior (Vol. 2, pp. 173–183). Oxford: Elsevier/Academic Press.

13.Goleman, D. (1995). Emotional intelligence. New York: Bantam Books.

14.Goleman, D. (2006). Social intelligence: the new science of human relationships. New York: Bantam Books.

15.Hurd, K., & Noller, P. (1988). Decoding deception: A look at the process. J Nonverbal Behav , 12, 217–233.

16.James, P. J., Lenartowicz, T., & Apud, S. (2006). Cross-Cultural Competence in International Business: Toward a Definition and a Model. Journal of International Business Studies , 37 (4), 525–43.

17.Mayer, J. D., & Salovey, P. (1997). What is emotional intelligence? In P. Salovey, & D. Sluyter, Emotional development and emotional intelligence: Implications for educators (pp. 3–31). New York: Basic Books.

18.Mayer, J. D., DiPaolo, M., & Salovey, P. (1990). Perceiving affective content in ambiguous visual stimuli: a component of emotional intelligence. J Pers Assess , 54, 772–781.

19.McCornack, S. A., & Parks, M. R. (1990). What women know that men don’t: sex differences in determining the truth behind deceptive messages. J Soc Pers Relat , 7, 107–118.

20.Pazian, M. (2014, January 03). The Benefits of Truth within Leadership. Retrieved 2017, from Elearning! Magazine:

21.Porter, S., ten Brinke, L. M., Baker, A., & Wallace, B. (2011). Would I lie to you? “leakage” in deceptive facial expressions relates to psychopathy and emotional intelligence. Pers Individual Dif , 51, 133–137.

22.Schmidt, K. L., Bhattacharya, S., & Denlinger, R. (2009). Comparison of Deliberate and Spontaneous Facial Movement in Smiles and Eyebrow Raises. J Nonverbal Behav , 33, 35–45.

23.Skinner, M., & Mullen, B. (1993). Facial Asymmetry in Emotional Expression – a Metaanalysis of Research. British Journal of Social Psychology , 30, 113–124.

24.Thomas, G., & Fletcher, G. J. (2003). Mind-reading accuracy in intimate relationships: Assessing the roles of the relationship, the target, and the judge. J Pers Soc Psychol , 85, 1079–1094.

25.Tupes, E. C., & Christal, R. E. (1961). Recurrent Personality Factors Based on Trait Ratings. Personnel Laboratory, Air Force Systems Command, Lackland Air Force Base, TX.

26.Van Dyne, L., Ang, S., Ng, K. Y., Rockstuhl, T., Tan, M. L., & Koh, C. (2012). Sub-dimensions of the four factor model of cultural intelligence: Expanding the conceptualization and measurement of cultural intelligence. Social and personality psychology compass , 6 (4), 295-313.

27.Van Rooy, D. L., Alonso, A., & Viswevaran, C. (2005). Group differences in emotional intelligence scores: Theoretical and practical implications. Pers Individ Dif , 38, 689–700.

28.Vrij, A., & Mann, S. (2004). Detecting deception: The benefit of looking at behavioral, auditory and speech content related cues in a systematic manner. Group Decision and Negotiation , 13 (1), 61–79.

29.Wojciechowski, J., Stolarski, M., & Matthews, G. (2014). Emotional Intelligence and Mismatching Expressive and Verbal Messages: A Contribution to Detection of Deception. PLoS ONE , 9 (3).

30.Zeidner, M., Matthews, G., & Roberts, R. D. (2009). What we know about Emotional Intelligence: How it affects learning, work, relationships and our mental health. Cambridge: MIT Press.





Gardner (1983) challenged the conventional view of intelligence by proposing seven (later extended to nine) types of intelligence and how people learn. Those with naturalistic intelligence are smart in dealing with the natural world while those with existential intelligence as asking deep moral questions; those with visual-spatial intelligence think in terms of physical space; those with bodily-kinaesthetic intelligence have a keen sense of their body; those with musical intelligence are sensitive to sound; those with interpersonal intelligence are better at interacting with others; those with intrapersonal intelligence are in tune with their inner feelings; linguistic intelligence refers to those good with words; and logical-mathematical intelligence describes higher ability to reason and calculate (Gardner, 1983).

Multiple intelligence are hard to measure and difficult to assess (Luskin, 2013). While Gardner (1983) proposed a limited set of intelligence, intelligence is not black and white. Emmons (2000), for example, considered spiritual intelligence an extension to Gardner’s concept of multiple intelligence. Marty Klein is a researcher on sexual intelligence (Kerner, 2012). This paper limits its analysis to three types of intelligence – EQ, CQ and PQ.

This paper refers to intrapersonal intelligence as a subset of Emotional Intelligence (EQ), whereas interpersonal intelligence is referred to as People Intelligence (PQ). This paper first discusses the meaning of three types of intelligence that are rapidly gaining importance in the field of crime scene investigation techniques. These are Emotional Intelligence (EQ), Cultural Intelligence (CQ) & People Intelligence (PQ). The significance of these intelligences within investigation techniques is discussed in detail.


Emotional Intelligence (EQ) is a psychological concept that defines an individuals’ ability to identify, understand, use and manage their emotions in a way that helps relieve stress, empathize and communicate effectively with other people, defuse conflict and overcome challenges (Mayer & Salovey, 1997). This ability allows individuals to understand and recognize what other people are experiencing emotionally. For the most part, this understanding and recognition is a nonverbal process that influences how well individuals connect with other people. It also influences people’s thinking about others. EQ differs based on an individual’s intellectual ability.

Golemon (1998) indicated that unlike intellectual ability, which is acquired, EQ is learned. Golemon (1998) and other proponents of this new psychometric, including psychologists Mayer & Salovey (1997) emphasize that EQ exists innately in certain individuals. Golemon (1998) added that everyone has a certain level of EQ and have the ability to monitor their own emotional states, emotions and enhance their EQ. Golemon (1998) suggests that society, both the private and public sector, should dedicate more resource towards research and programs that would help people develop EQ. Other researchers (Mayer & Salovey, 1997) regard EQ as a skill that combines emotions (feelings) and cognitions (thoughts). Mayer & Salovey (1997) placed EQ within the context of well-being, health and personality.

EQ is defined by four key attributes: self-awareness, self-management, social awareness, and relationship management (Chong, Lee, Roslan, & Baba, 2015). Self-awareness is an individual’s ability to recognize their emotions and how they affect their thoughts. Self-management is the individuals’ ability to control behaviors and impulse feelings and manage their emotions in healthy way, follow through on commitments, take initiatives, and adapt to changing situations and circumstances. On the other hand, social awareness is the ability of individuals to understand their needs; emotions; other people’s concerns; pick up on emotional cues; recognize the power of organization or group’s dynamics and feel comfortable socially. Lastly, relationship management is the individual’s ability to communicate clearly, develop and maintain good relationships, influence and inspire other people, manage conflict and work well with team members.

A large body of research has suggested a possible link between EQ and criminal behaviour suggesting that criminal psychologists can understand criminal behaviour by understanding their EQ and ultimately profiling a criminal accordingly (Caspi,, 1994; Eysenck, 1996; Gottfredson & Travis, 1990; Hayes & O’Reilly, 2013; Lynam, 1993; Megreya, 2013; Puglia,, 2005; Sharma,, 2015).


Cultural Intelligence (CQ) refers to the ability of an individual ability to recognize and understand values, behaviors, customs, values and languages of a people and to apply that knowledge in order to achieve specific goals. It enables an individual to work and relate effectively across culture (James, Lenartowicz, & Apud, 2006). This tool can help improve an individual’s performance in different cultural settings and identify meanings that could be misunderstood or lost in translation in non-verbal behavior. Securing and using CQ can enable an investigator to function effectively in multicultural settings, blend into the community and gain acceptance and thus, conduct a successful investigation.

Cultural Intelligence (CQ) impacts the person’s ability to interact with different cultures in an effective manner. A popular model of CQ proposed by Earlye & Ang (2003) has four dimensions – Cognitive, Meta-cognitive, Motivational, and Behavioral. Cognitive focuses on the person’s knowledge of cultural practices; Meta-cognitive focuses on the awareness of cultural background during interpersonal interactions; Motivational focuses on the individual’s drive to learn more about culture; and Behavioral aspect focuses on their verbal and non-verbal abilities (Ward, Fischer, Lam, & Hall, 2009).


The Big Five personality traits, or the Five Factor Model (FFM) is a well-known model that describes personality. The model was initially proposed by Tupes & Christal (1961) and later improved by Digman (1990). The five factors are Openness, Conscientiousness, Extraversion, Agreeableness and Neuroticism, also known as OCEAN. Openness is the curiosity to experience something new; Conscientiousness is the tendency to be either organized or careless; Extraversion explains whether the person is outgoing or socially reserved; Agreeableness describes friendliness or compassion against being detached; and Neuroticism is about being sensitive or nervous against being secure and confident.

People Intelligence (PQ) makes individuals aware of the inner motivations of people they interact with in everyday life. Individuals with high PQ have the ability to perceive what makes their coworkers, friends, and family tick. They can read non-verbal behaviour and body language of other people and accurately weigh choices they are presented with in work, family life and relationships and accurately judge whether their personal life goals to together well or conflict. Police detectives and other investigators with high PQ are inquisitive about people, open to own experiences, show willingness to change themselves can anticipate their actions and desires, and ultimately predict behaviors of offenders or criminals (Zacker & Bard, 1973).


It is suggested that EQ can help criminal investigators recognize to a certain extent, intentions of other people and consequently in determining whether an individual is being manipulative. As such, investigators can use EQ as an element of investigation. As revealed in multiple studies, EQ can provide detectives with clues about offenders and their mode of operation. For example, the FBI investigator, Robert Ressler became sensitive to significant difference in mode of operations between John Gacy and Ted Bundy. Bundy would first use a blunt strike to know out victims. On the contrary, Gacy would use deceit to kill his victims (Guy, 2016). On this basis, an investigator can check whether the offender’s victim was prone to abuse or deceit. The investigator can also determine the mode of operation of the perpetrator (Mayer, Caruso, & Salovey, 2000).

Elsewhere, it has been argued that higher EQ is a predictor of satisfaction in life (Mayer et al., 2000). Mayer et al. (2000) believe that individuals with high EQ are more likely to exhibit healthier psychological adaption because such people demonstrate adaptive defense behavior against adaptation. Similarly, studies on performance measures of EQ have suggested that higher EQ levels can be associated with increased and improved relationships with family and friends. On the contrary, lower EQ have been associated with problematic behavior and unfavourable interactions with family and friends (Mayer et al., 2000). Lower EQ was associated with trouble-prone behavior and lower self-reported violent behavior among college students (Mayer et al., 2000). In Mayer et al. (2000), lower EQ was measured using Mayer-Salovey-Caruso Emotional Intelligence (MSCEIT) and found to associate with increased involvement in deviant behavior, including the involvement in vandalism and physical fights and increased use of alcohol and illegal drugs. Erasmus (2007) revealed that individuals who are lacking in emotional and social competence lack the ability to relate and empathize with others and are self-centered. Erasmus (2007) also found that students with delinquency problems (i.e., participate in crimes, sale drugs, engage in sexual behavior, truancy, dishonesty and pornography) experience emotional and personal problems. A prospective study by Fortin (2003) further revealed that students with delinquency behavior lack self-control and that this makes unable to accept other people and react to criticism in a better way. Fortin (2003) also suggested that a lack of the ability to control moods and emotions makes these delinquent students to conflict with adults and other students. In another study investigating the EQ-delinquency behavior, Chong et al. (2015) confirmed that students with higher delinquency behavior had lower EQ than the normal students.

Researchers in various fields including criminology, sociology and psychology have also suggested a possible correlation between criminal behavior and EQ with remarkable interests being given to personality and intelligence (Frisell, Pawitan, & Langstrom, 2012; Lynam, Moffitt, & Stouthamer-Loeber, 1993; Eysenck, 1996). Two studies (Frisell, Pawitan, & Langstrom, 2012; Lynam, Moffitt, & Stouthamer-Loeber, 1993) suggested that criminal offenders tend to have lower EQ than non-offenders. Other studies have associated criminal behavior with personality variables including low level of self-control; high level of adverse emotionality; and high levels of neuroticism, psychoticism and extraversion; and difficulty in impulse control (Mottus et al., 2012; Caspi et al., 1994; Gottfredson & Travis, 1990).

Cultural orientation has been found to influence criminal violence because they are supportive of violence (Messner, 1988). Culture is a set of values and beliefs. Therefore they can be learned through social interactions and passed on through groups and across generations (Holt, 2009). Ferrell (1995) states that criminal behaviour is subcultural behaviour, whether carried out by an individual or a group. Research has found a relationship between criminal acts and symbolism, which is commonly found in criminal subcultures. This highlights the importance of CQ in criminal profiling.

A recent study by Gottfredson and Travis (1990) associated criminal behavior with high level of Neuroticism and low levels of Agreeableness, Conscientiousness, and Openness (PQ). Other studies found conflicting results regarding correlation between criminal behavior and EQ. Results by Moriarty et. al. (2001) showed that adolescent sex offenders and age-matched none-offenders had similar EQ variables. However, the Trait Meta-Mood Scale (TMMS) revealed a deficit in offenders’ attention to feeling. In another study, Puglia et al. (2005) did not find significant difference between controls and adult offenders in EQ, as measured by MSCEIT. However, in Puglia et al. (2005) sex offenders had a higher score than none-sex offenders on a MSCEIT scale. In Hayes and O’Reilly (2013), 26 male juveniles were found to have lower EQ than 30 control male juveniles. On the contrary, Hemmati et al. (2004) found adult male offenders to have higher trait EQ levels than the normative sample of Emotional Question Inventory (Hoaken et al., 2007; Owen, & Fox, 2011; Megreya, 2013). Other studies supporting low EQ in offender found that violent perpetrators had low score than nonviolent offenders in empathy and facial expression recognition (Hoaken et al., 2007; Owen, & Fox, 2011). EQ was also found to strongly correlate with criminal thinking styles (Megreya, 2013). Megreya et al. (2012) found EQ to correlate with criminal styles of thinking, which differed with the types of offense. Violent offenders were found to experience more problems on multiple components of EQ than offenders, including social problem solving, personal control, self-regulation, mental health, and emotional stability (McMurran et al., 2001; Ross, & Fontao, 2007; Mak, 1991; Jones et al., 2007). Elsewhere, Megreya (2013) examined the link between criminal behavior and EQ using samples of Egyptian adult none-offenders and offenders. Megreya (2013) further examined the possible correlation between EQ and types of offenses by dividing offenders who had sentenced into three categories: those sentenced for murders, drugs, and theft. Results were in conformity with indirect and direct aggression theory that physical aggression requires less social intelligence than indirect aggression. According to developmental theory of aggressive behavior, direct verbal aggression requires less social intelligence than indirect aggression, and physical aggression requires more social intelligence than direct verbal aggression (Fisher, Beech, & Browne, 1999). This theory suggests that high EQ levels constrain individuals from participating in criminal activities. It was suggested that EQ training should be included in the forensic intervention programs. Elsewhere, it was suggested that criminal behavior could be minimized by improving on components of EQ, including facial expression recognition, social problem solving and anger management (Penton-Voak et al., 2013; Walters, 2008; Nelis et al., 2009).

Sharma et al. (2015) examined the relationship between criminal behavior and low levels of EQ using a sample of 202 subjects. The sample consisted of 101 matched normal controls and 101 convicted offenders. The offender group was picked from a jail and consisted of persons convicted of robbery, rape, murder and other different crimes. The control groups and the intervention groups were matched on gender, marital status, occupation, education, and age and assessed on Mangal Emotional Intelligence Inventory (MEII) and General Health Questionnare-12. The convicted offenders group received significantly lower score on MEII domains than the control group. These domains include interpersonal awareness (other emotions), intrapersonal awareness (own emotions), interpersonal management (other emotions), intrapersonal management, and aggregate emotional quotient.

Canter (1994) identified crime as a form of interpersonal relations/connection, involving one person observing specific ways via which an offender treats the victim. He emphasized the dependency between personality traits and behaviors of a criminal. Canter (1994) likens crime to theoretical performance. Canter (1994) contests that criminal offenders use violence to dramatically write for themselves and cast their crime victims in three key roles: people, vehicles and objects.

This discussion explains how an investigator without adequate understanding of EQ, CQ and PQ could easily misinterpret a person’s behaviour.


As confirmed in this paper, findings from several studies discussed in this paper suggest that cyber profiling can be improved by adding the element of EQ, CQ and PQ as forensic experts can interview offenders with a view to determine their EQ. Certain criminal investigations could further benefit from sexual and spiritual intelligence which might reveal motives behind the criminal activity.

This paper analyzed how multiple intelligences (in particular EQ, CQ and PQ) could make criminal investigation more effective. EQ is useful to understand one’s own emotions and this helps the investigator defuse interpersonal conflicts. EQ teaches the importance of self-awareness and how this ability could help the investigator pick up vital emotional cues when interviewing people.

CQ is equally important because people from different ethnic origins display different behaviour. The knowledge of different customs will allow the investigator to behave in a suitable manner and not jeopardize the investigation by giving out wrong signals. Investigation of certain crimes requires a good deal of people interaction. PQ is vital because not knowing personality traits could lead the investigator in the wrong track.

Considering these studies it is clear that the importance of EQ, CQ and PQ within investigation techniques cannot be denied. A person’s openness or the lack of it, their cultural background could be valuable information to understand them. These are indicators of certain personality traits but cannot be interpreted as a judgment of their behaviour.


1.Canter, D. (1994). Criminal Shadows. HarperCollins Publishers Ltd.

2.Caspi, A., Moffitt, T. E., Silva, P. A., Stouthamer-Loeber, M., Krueger, R. F., & Schmutte, P. S. (1994). Are some people crime-prone? Replications of the personality-crime relationship across countries, genders, races, and methods. Criminology , 32, 163–96.

3.Chong, A. M., Lee, P. G., Roslan, S., & Baba, M. (2015). Emotional Intelligence and At-Risk Students. SAGE Open.

4.Digman, J. M. (1990). Personality structure: Emergence of the five-factor model. Annual Review of Psychology , 41, 417–440.

5.Earley, C. P., & Ang, S. (2003). Cultural Intelligence Individual Interactions Across Cultures. Stanford University Press.

6.Emmons, R. A. (2000). Is Spirituality an Intelligence? Motivation, Cognition, and the Psychology of Ultimate Concern. International Journal for the Psychology of Religion , 10 (1), 3-26.

7.Erasmus, C. P. (2007). The role of emotional intelligence in the adaptation of adolescents boys in a private school. University of South Africa, Pretoria.

8.Eysenck, H. J. (1996). Personality and crime: where do we stand? Psychol Crime Law , 2, 143–52.

9.Ferrell, J. (1995). Culture,Crime, and Cultural Criminology. Journal of Criminal Justice and Popular Culture , 3 (2), 25-42.

10.Fisher, D., Beech, A., & Browne, K. (1999). Comparison of sex offenders to no offenders on selected psychological measures. International Journal of Offender Therapy and Comparative Criminology , 43, 473–491.

11.Fortin, L. (2003). Students’ antisocial and aggressive behaviour: Development and prediction. Journal of Educational Administration , 41, 669-688.

12.Frisell, T., Pawitan, Y., & Langstrom, N. (2012). Is the association between general cognitive ability and violent crime caused by family-level confounders? PLoS ONE .

13.Gardner, H. (1983). Frames of Mind: The theory of multiple intelligences. New York: Basic Books.

14.Goleman, D. (1998). Working with emotional intelligence. New York: Bantam.

15.Gottfredson, M. R., & Travis, H. (1990). A general theory of crime. Stanford, CA: Stanford University Press.

16.Guy, F. (2016, March 12). Robert Ressler: Psychological Profiling of Serial Killers. Retrieved 2017 from

17.Hayes, J. M., & O’Reilly, G. (2013). Psychiatric disorder, IQ, and emotional intelligence among adolescent detainees: a comparative study. Legal Criminological Psychology , 18, 30–47.

18.Hemmati, T., Mills, J. F., & Kroner, D. G. (2004). The validity of the Bar-On emotional intelligence quotient in an offender population. Personality and Individual Differences , 37, 695–706.

19.Hoaken, P. N., Allaby, D. B., & Earle, J. (2007). Executive cognitive functioning and the recognition of facial expressions of emotion in incarcerated violent offenders, non-violent offenders, and controls. Aggress Behav , 33, 412–421.

20.Holt, T. (2009, December). Cultural Theories. Criminology .

21.James, P. J., Lenartowicz, T., & Apud, S. (2006). Cross-Cultural Competence in International Business: Toward a Definition and a Model. Journal of International Business Studies , 37 (4), 525–43.

22.Jones, A. P., Forster, A. S., & Skuse, D. (2007). What do you think you’re looking at? Investigating social cognition in young offenders. Crim Behav Ment Health , 17, 101–106.

23.Luskin, B. J. (2013, Sep 30). How are you smart? Retrieved 2017 from

24.Lynam, D. R., Moffitt, T. E., & Stouthamer-Loeber, M. (1993). Explaining the relation between IQ and delinquency: class, race, test motivation, school failure, or self-control? J Abnorm Psychol , 102, 187–196.

25.Mak, A. S. (1991). Psychosocial control characteristics of delinquents and nondelinquents. Crim Justice Behav , 18, 287–303.

26.Mayer, J. D. (2013). Personal Intelligence: The Power of Personality and How It Shapes Our Live. NY: Scientific American.

27.Mayer, J. D., & Salovey, P. (1997). What is Emotional Intelligence. In P. Salovey, & D. J. Sluyter (Eds.), Emotional Development and Emotional Intelligence: Educational Implications (pp. 3-34). New York: Harper Collins.

28.Mayer, J. D., Caruso, R., & Salovey, P. (2000). Emotional intelligence meets traditional students for an intelligence. Intelligence , 27, 267-298.

29.McMurran, M., Fyffe, S., McCarthy, L., Duggan, C., & Latham, A. (2001). Stop & Think! Social problem-Solving therapy with personality-disordered offenders. Crim Behav Ment Health , 11, 273–85.

30.Megreya, A. M. (2013). Criminal thinking styles and emotional intelligence in Egyptian offenders. Crim Behav Ment Health , 23, 56–71.

31.Megreya, A. M., Bindemann, M., & Brown, A. (2012). Criminal thinking in a Middle Eastern prison sample of thieves, drug dealers and murderers. Leg Criminological Psychol Forthcoming .

32.Messner, S. F. (1988). Research on cultural and socioeconomic factors in criminal violence. Psychiatr Clin North Am. , 11 (4), 511-525.

33.Moriarty, N., Stough, C., Tidmarsh, P., Eger, D., & Dennison, S. (2001). Deficits in emotional intelligence underlying adolescent sex offending. J Adolesc , 24, 743–51.

34.Mottus, R., Guljajev, J., Allik, J., Laidra, K., & Pullmann, H. (2012). Longitudinal associations of cognitive ability, personality traits and school grades with antisocial behavior. Eur J Pers , 26, 56–62.

35.Nelis, D., Quoidbach, J., Mikolajczak, M., & Hansenne, M. (2009). Increasing emotional intelligence: (how) is it possible? Personality Individ Differ , 47, 36–41.

36.Owen, T., & Fox, S. (2011). Experiences of shame and empathy in violent and non-violent young offenders. J Forens Psychiatry Psychol , 22, 551–63.

37.Penton-Voak, I. S., Thomas, J., Gage, S., McMurran, M., McDonald, S., & Munaf, M. (2013). Increasing recognition of happiness in ambiguous facial expressions reduces anger and aggressive behaviour. Psychol Sci , 24 (5), 688–97.

38.Puglia, M. L., Stough, C., Carter, J. D., & Joseph, M. (2005). The emotional intelligence of adult sex offenders: ability based EI assessment. J Sex Aggress , 11, 249–58.

39.Ross, T., & Fontao, M. I. (2007). Self-regulation in violent and non-violent offenders: a preliminary report. Crim Behav Ment Health , 17, 171–8.

40.Sharma, N., Prakash, O., Sengar, K. S., Chaudhury, S., & Singh, A. R. (2015). The relation between emotional intelligence and criminal behavior: A study among convicted criminals. Ind Psychiatry J. , 24 (1), 54-58.

41.Tupes, E. C., & Christal, R. E. (1961). Recurrent Personality Factors Based on Trait Ratings. Personnel Laboratory, Air Force Systems Command, Lackland Air Force Base, TX.

42.Walters, G. D. (2008). Anger management training in incarcerated male offenders: differential impact on proactive and reactive criminal thinking. Int J Forensic Ment Health , 8, 214–7.

43.Ward, C., Fischer, R., Lam, F. S., & Hall, L. (2009). The Convergent, Discriminant, and Incremental Validity of Scores on a Self-Report Measure of Cultural Intelligence. Educational and Psychological Measurement , 69 (1).

44.Zacker, J., & Bard, M. (1973). Effects of conflict management training on police performance. Journal of Applied Psychology , 58 (2), 202-208.






The increase in the number of people using networked digital devices has led to incidences of crime that call for forensic investigations (Brown, 2015). The existence of Cyber Forensics skills has made it possible to gather evidence from such devices. The evidence collected is used in courts to establish the crime and bring Cyber criminals to justice. Cyber Forensic investigators and analysts are often entrusted with the task of finding, recording, analysing, and reporting of digital evidence. The whole process of gathering forensic evidence has a number of challenges. These challenges are categorized into five broad areas: hardware challenges, software challenges, cloud forensic challenges, legal challenges and human challenges (Karie, & Venter, 2015; Lindsey, 2006; Mohay, 2005).


Hardware challenges are linked to the needs of the modulated technology and enhancements of the hardware. Studies suggested that some criminal suspects change the hard disk within their devices before the Cyber Forensic expert can gain access to the device (National Institute of Justice, 2002; Brown, 2015). In such cases, the suspects use the write blockers to shift information between the two hard disks. The main effect is that a forensic examination of the new hard disk, may not display some of the relevant evidence. On the other hand, the evidence gathered from the new hard disk will lack consistency, and may not be apparent (Brown, 2015; Spafford, 2006).

Further, the evidence gathered from a device that was reset, may accentuate the problem since during the reset process, a small portion of the backup information is likely to have been reinstalled. For example, different mobile devices have hard disks that have enmeshed algorithm that are responsible for erasing the data automatically. Since the technology for collecting information from unused devices or devices where information was deleted by a user is still under development, there is likely to be some delays in obtaining such information. It is for this reasons that some Cyber Forensic experts have reported tremendous challenges in retrieving information from content that was deleted from the device (Spafford, 2006).


The current era of technological advancements and changes in gathering forensic evidence has resulted into the birth of Platform as a Service (PaaS) and Software as a Service (SaaS), which have brought a number of changes into the computing structure. The use of new software and new technology has brought about a number of challenges. One of the challenges is lined to the well-developed device operating system. The current operating systems have been log enabled, and now requires a Cyber Forensic expert to gather background information on the device, which includes the information on accessibility of the application, usage of the application, and the level of information provided by the specific user of the application. Even though the new development appears like a progress for the different devices, the development requires some time for it to mature (Spafford, 2006; Giordano & Maciag, 2002).

Several challenges have been reported on the application accessibility since the application and the operating system are defined differently (Giordano & Maciag, 2002). For example, any alteration made on the file content may not be tracked until it is compared with subsequent/previous file versions or, if it is compared with the modified version of the time stamp. In case the Cyber Forensic expert suspects some manipulation on the document, it would be a challenge to determine the extent of manipulation (Brown, 2015).

Further, some forms of applications and log information that are collected by the application or the operating system, could be useful as evidence in certain cases. Despite the usefulness of the application, the awareness of its use is still at an infant stage making it difficult for the Cyber Forensic experts to ensure the effective use of the application. For example, an operating system like Windows 8 will collect information on all the Wi-Fi networks that have been accessed together with the transmission of the data. The information gathered would help investigations, such as those investigations that involve theft of data or in cases of network intrusion. However, a correlation between the gathered information, from the sources, and the event violation in the gathered information is a concept under research and experimentation (Giordano & Maciag, 2002).

The high number of mobile messaging applications available across the globe uses a software that automatically erase the information that is shared. The main challenge here is that it will be complex for a Cyber Forensic expert to gather such information that was deleted. Another challenge is the encryption in different mobile devices with intention of having the information protected especially during the process of gathering data. For example, gathering data from encrypted mobile chat applications may pose a challenge in certain situations. Contrary to popular belief all mobile chat applications are not encrypted. Certain mobile chats allow a secure connection between the sender and the receiver with no option to retrieve the message after a set time period. Other sessions are simply saved as text messages in the phone storage allowing anyone with the mobile phone passcode to access all stored messages. Even without a passcode, it is technically possible for the chat server to provide chat history with the right encryption key. The decryption of devices may be a challenge to some investigations where the storage or device itself is encrypted (Giordano & Maciag, 2002).

Not handing over mobile device PIN and passwords could lead to legal consequences in certain countries. For example, not giving passwords can get someone arrested according to Schedule 7 of Terrorism Act in the United Kingdom (, 2008; Mandhai, 2017).


Cloud computing is now used by smart mobile devices. The flexibility and scalability of cloud computing poses a huge challenge to forensic investigation (Lopez, Moon, & Park, 2016). The data in these devices, maybe able to be accessed everywhere hence posing another challenge to the investigators. It is a challenge for the investigator to locate the data in a way that ensures the privacy rights of the users. The investigators require the knowledge on anti-forensic tools, practices, and tools that help ensure that the forensic analysis is done accordingly (Spafford, 2006; Lopez, Moon, & Park, 2016).

Cloud-based applications also enable users to ensure that data is accessed from various devices. For example, if one of the two devices of a single user is compromised and both devices lead to some changes in the application, it would be difficult for the Cyber Forensic expert to identify the real source of the change. High risks may compromise credentials and theft of the identity in an environment that is cloud-based and lead to changes that are unknown such as the evidence remaining unknown. On the other hand, an email viewed using a user’s smart mobile device and deleted may not be traced easily. In most cases, it would be difficult to examine severs of the mail and identify the evidence of the deleted communication (Lopez, Moon, & Park, 2016).


There have been some changes in the data protection and privacy regulations in different countries across the globe (Garrie & Morrissy, 2014). Cyber laws and regulations in different jurisdiction vary and many do not take into account, the complexity in collecting forensic evidence. For example, in the machine of a suspect, the information that is available is likely to have some personal information that could be crucial in an investigation. However, accessibility to such private information is likely to be considered as a violation of user privacy (Spafford, 2006).

On the other hand, the era of companies giving some provision to their employees to use their individual devices in accessing the official communication is likely to contribute to several challenges involved in data gathering. Accessing the email of a user, for instance, using webmail and a smart mobile device together with downloading the involved attachments is an example of theft of personal data. In the current era, collecting specific information from a user device is in itself a challenge (Kaur & Kaur, 2012).


Cyber Forensic experts are tasked with collecting and analysing the role of identifying criminals and going through all the evidence gathered against the criminals. These are well-trained professionals working for the public law enforcement agencies or in the private sector to perform roles that are associated to the collection and analysis of forensic evidence. The Cyber Forensic experts also come up with reports that are majorly used in the legal settings for investigations. Besides working in the laboratory, Cyber Forensic experts take up the role of applying the techniques of forensic investigation in the field uncovering the data that is relevant for the court (Karie & Venter, 2015).

The Cyber Forensic experts have the ability of recovering data, which was deleted previously, hidden in the mobile folds, or encrypted. The court, in most cases, calls the Cyber Forensic experts to provide testimony in the court and elaborate on the evidence reports during a given investigation. As such, the Cyber Forensic investigators get involved in complicated cases that may include examining Internet abuse, determining the digital resources that are misused, verifying the offenders’ alibis, and examining how the network was used to come up with forensic threats. There are times when the Cyber Forensic expert is expected to offer support to cases that deal with intrusions, breaching of data, or any form of incident. Through the application of the relevant software and techniques, the device, system or the platform is examined for any kind of evidence on the persons involved on the crime (Karie, & Venter, 2015).

In a forensic examination, data is retrieved from the digital devices, which are considered to be evidence required for the investigations. In most cases, a systematic approach may be used to analyse the evidence, which would be presented in the court at the time of the proceedings. At an early stage of the investigation, the Cyber Forensic expert is required to get involved in gathering evidence. Early engagement in the investigation process helps the Cyber Forensic expert to be in a position to restore all the content without causing damage to the integrity (Karie, & Venter, 2015).

There are different types of forensic cases that are handled by the Cyber Forensic experts. Some of the cases deal with intruders getting into the victim’ devices and stealing their data, other cases, are for the crime offenders who launch attacks on several websites or those who try to gain some access to the names of the users and the password so as to engage in identity fraud. A Cyber Forensic expert has the ability to explore the type of fraud committed by analysing the evidence and using the required techniques. Despite the reason behind the investigation, the experts go through the process procedurally to ensure the findings recorded or gathered are sound. After opening a given case, the items that would be seized include the digital devices, software, and other media equipment’s so as to run the investigation. In the retrieval process, the items considered essential will be gathered so as to give the analyst everything that would be required for the testimony (Karie, & Venter, 2015).

Another human-related challenge faced by Cyber Forensics is spoliation (Cavaliere 2001; Mercer 2004). Spoliation occurs when the person handling evidence fails to preserve, alters evidence, or destroys evidence that could be useful in pending ligation (Watson, 2004). Spoliation may be caused by negligent on the part of the party handling the litigation or handling evidence and intentional destroying evidence by the handler.


Elsewhere, in a literature-based study, Karie and Venter (2015) identified and categorized cyber forensic challenges into four: technical challenges, law enforcement or legal system challenges, personal-related challenges and operational challenges.

Technical Challenges were identified as vast volume of data; bandwidth restrictions; encryption; volatility of digital evidence; incompatibility among heterogeneous forensic techniques; the digital media’s limited lifespan; emerging devices and technologies, sophistication of digital crimes; anti-forensics; emerging cloud forensic challenge.

Legal Challenges were identified as jurisdiction, admissibility of digital forensic techniques and tools; prosecuting digital crimes; privacy; ethical issues; lack of sufficient support for civic prosecution or legal criminal prosecution.

Personnel-related Challenges were identified as semantic disparities in Cyber Forensics; insufficient qualified Cyber Forensic personnel; insufficient forensic knowledge and the reuse among personnel; strict Cyber Forensic investigator licensing requirements; and lack of formal unified digital forensic domain knowledge.

Lastly, Operational Challenges were identified as significant manual analysis and intervention; incidence detection, prevention and response; lack of standardized procedures and processes; and trust of Audit Trails (Vaciago, 2012; Mercuri, 2009; Bassett, Bass, & O’Brien, 2006; Liu, & Brown, 2006; Richard, & Roussev, 2006; Arthur, & Hein, 2004; Mohay, 2005).


This paper revealed several challenges faced by Cyber Forensics. These challenges can be categorized into five: hardware, software, cloud, legal and human. They can also be categorized into technical challenges, law enforcement or legal system challenges, personal-related challenges, and operational challenges. While the available literature has sufficient details on the technical aspects of Cyber Forensic investigation, the human element only seems to touch the surface. There is a huge gap in terms of understanding the emotional and cultural aspects of the stakeholders involved in the investigation process. This calls for a review of Cyber Forensics where elements of Emotional Intelligence (EQ), Cultural Intelligence (CQ) and People Intelligence (PQ) are further investigated for a better understanding.


  1. Arthur, K.K., & Hein, S.V. (2004). An investigation into computer forensic tools. Proceedings of the ISSA conference; Midrand, South Africa. Piscataway, NJ: IEEE Computer Society Publishers; 1–11.
  2. Bassett, R., Bass, L., & O’Brien, P. (2006). Computer forensics: an essential ingredient for cyber security. J Inform Sci Technol; 3:22–32.
  3. Brown, C. (2015) Investigating and prosecuting cybercrime: Forensic dependencies and Barriers to Justice. International Journal of Cyber Criminology, 9 (1): 55-119.
  4. Cichonski, P., Millar, T., Grance, T., & Scarfone, K. (2012). Computer security incident handling guide. Revision 2. National Institute of Standards and Technology; 2012 Aug.; NIST Special Publication 800-61
  5. Cavaliere, F. J. (2001). “The Web-wise Lawyer,” Practical Lawyer; 47(4): 9-10.
  6. Garrie, D. & Morrissy, D. (2014). Digital forensic evidence in the courtroom: Understanding content and quality. Northwest Journal of technology and intellectual property, 12 (2): 121.
  7. Giordano, J & Maciag, C. (2002). Cyber forensic: A military operations perspective. International Journal of digital evidence, 1 (2): 1-13.
  8. Kaur, R & Kaur, A. (2012). Digital Forensics. International Journal of Computer Application, 50(5): 0975-887.
  9. Karie, N.M., & Venter, H.S. (2015). Taxonomy of challenges for digital forensics. Journal Forensics, Sci, 60(4): 885-893.
  10. Liu, V., & Brown, F. (2006). Bleeding-edge anti-forensics. Proceedings of the InfoSec World Conference & Expo; Orlando, FL. Washington, DC: NIST Special Publication; 800–86.
  11. Lopez, E.M. & Moon, S.Y., & Park, H.J. (2016). Scenario-Based Digital Forensics Challenges in Cloud Computing. Symmetry, 8 (107): 2-20.
  12. Lindsey, T. (2006). Challenges in Digital Forensics. Retrieved on 8th May 2017 from
  13. (2008). Counter-Terrorism Act 2008. Retrieved May 23, 2017, from
  14. Mandhai, S. (2017, May 15). Cage activist faces charges for not giving up passwords. Retrieved May 23, 2017, from
  15. Mercer, L. D. (2004). “Characteristics and Preservation of Digital Evidence,” FBI Law Enforcement Bulletin 73(3): 28-34.
  16. Mercuri, R. (2009). Criminal defense challenges in computer forensics. Proceedings of the Digital Forensics and Cyber Crime Conference, Albany, NY. Berlin/Heidelberg: Springer Berlin Heidelberg Publishers.
  17. Mohay, G. (2005). Technical Challenges and Directions for Digital Forensics in 1st International Workshop on Systematic Approaches to Digital Forensic Engineering.
  18. National Institute of Justice. (2002). Results from Tools and Technology Working Group, Governors Summit on Cybercrime and Cyber terrorism, Princeton NJ.
  19. Richard, G.G., & Roussev, V. (2006). Digital forensics tools – the next generation.
    Hershey, PA: Idea Group Inc; 76–91.
  20. Vaciago, G. (2012). Cloud computing and data jurisdiction: a new challenge for
    digital forensics. Proceedings of the third International Conference on Technical and Legal Aspects of the e-Society; Valencia, Spain. IARIA XPS Press; 7–12.
  21. Spafford E. (2006). Some Challenges in Digital Forensics. In: Olivier M.S., Shenoi S. (eds) Advances in Digital Forensics II. IFIP Advances in Information and Communication, vol 222. Springer, Boston, MA
  22. Watson, L. M. (2004). “Anticipating electronic discovery in commercial cases,” Michigan Bar Journal. 83(31), 23-45.





In the current era of technological advancements, there is an increasing interest of testing the lie detection methods. Different scholars have gained interest in testing whether lie detection is a science or myth. What is evident is that, in most cases, liars may not offer telltale signs of their dishonesty. As such, it is difficult to identify the persons who are telling the truth or those who tell lies. In most cases, a lie could be embedded in some truth. There is also a small difference between the people telling the truth and those who tell lies. A common mistake that has been made by lie detectors is putting so much emphasis on the nonverbal cues. For example, lie detectors neglect the actions of an individual when he or she is telling the truth. They only record the actions of liars when they are lying. Lie detectors have also proved to be overly confident in their skill of detection (Ask, Granhag, Juhlin, & Vrij, 2013). In this case, there have been a number of misconceptions when it comes to deceptions. This paper discusses the fact that lie detection is science rather that a myth believed by some proponents.


Lying can be defined using many approaches. Lying is seen as communication that is falsified and intended to benefit only one party. This classification covers a broad range of subjects – from humans to plants. In a plant, deception may be experienced in a situation where a male wasp is seduced by orchid flower, which produces smell creating an illusion of mating. The gainer is the orchid because, in the course of deception, the wasp acts as an agent of pollination. This approach is not conventionally used because it includes an act of misleading as a way of deception. Lying can be defined as an act that is meant to manipulate other people believe something he or she knows is untrue (Zuckerman, DePaulo, & Rosenthal, 1981; Krauss, 1981). Lying is a part of everyday life, sometimes causing harm and sometimes “white lies” may even benefit the lie receiver by acting as a social lubricant (Vrij, 2008).


Vrij (2008) argues that everybody has an idea of what lying is. Everybody knows that lying is something that is not acceptable in the society. The myth here is pretending that we seldom lie because humans cannot accept themselves as miserable liars. Since lying is unacceptable in society, people opt for others means of deceit. In the long run, they spend little time with liars and completely avoid them. Most individuals in the world are sick liars; they forget that they are lying and reveal their deceit by being nervous or avoiding eye contact. The author notes that people tend to be good detectors when monitoring their children, and close friends. Criminals accomplish their objective by deceiving others. Then there are professional lie catchers who are technically trained to catch lies. There has been a revolution in technology with the development of machines technically designed to detect lies. An example is the brain-scanner, which is used by researchers to monitor the thoughts and feeling of somebody directly (Vrij, 2008).

There is a big difference between good liars and poor lie detectors. Most people assume that they don’t lie and by so doing they are underestimating their ability to lie. There are many ways of telling why people believe that they are the worst liars than they thought. First, they overestimate how honest they are with their feelings and thought to others. By saying so, Vrij (2008) implied that people believe that their lies trend all the way. Second, the selfish act makes people see themselves as more morally upright than others (Kaplar & Gordon, 2004). When someone admits that he or she is a good liar, he or she complicates the good self-image. People will tend to disclose their white lies and hide dangerous lies (Elaad, 2003). This shows that it is easy to detect a dangerous lie compared to white lie because people will remember the lies that can be easily noticed. Lastly, people will remember instances when they lied and were detected that than instances when they lied successfully. When people forget how easily they can lie, they are underestimating their ability to lie (Vrij, 2008).


The reason why people lies go unnoticed is that no efforts are put in detecting them, and they do not want to know the truth. Vrij (2008) calls it the ostrich effect and states that there are at least three reasons as to why people don’t like to know or accept the truth. One, the truth may be bitter and people prefer to stay ignorant by believing a more “pleasant” lie. Second, people fear consequences that the truth may present. They are afraid of what they would need to do if they were to accept the truth. Third, people fear not knowing what to do if they came to know the truth.


Even though, lie detection has been used in many contexts, the technique has been misunderstood in many ways. Lie detection is one complex technique and requires a personal judgment. The scientific underpinnings used in lie detection are less straightforward than other tests like the breath-alcohol test. The nature of lie detection makes it interesting to analyze especially from the basis of science. It uses the methods and conclusions of a number of disciplines that deal with behavior and human physiology. Lie detection also offers a vital probability issue that is applicable in criminal law (O’Sullivan, 2007).

In 1895, a mix of pulse reading and blood pressure was used to investigate crime. Other experiments done in lie detection have used blood pressure, and respiratory recordings. The science of lie detection was equally tested by the polygraph created by Larson John. The polygraph used the three measurements (pulse, blood pressure, and respiration) in lie detection. The Keeler, made by Leonarder Keeler, introduced the galvanic response of the skin to the list. The key improvement in the Keeler is the ability to obviate and record the blood pressure distortions especially in the readings that could result from muscular flexing (Evans & Stanovich, 2013).

In most cases, the procedure used in lie detection such as the polygraph test is done by experienced examiners in a controlled environment. The examiner will engage in questions that depend on the preliminary interview results together with different circumstances and facts that create an accusation basis. Some questions would be variable depending on the individual being questioned. Many studies on lie detection have proposed the use of some systematically designed models as ways of measuring the physiological activities and creating the last credibility judgment (Frank & Ekman, 2004).

Even though lie detection appears extremely useful, a number of results obtained from lie detectors have been excluded from trial (Albrechtsen, Meissner, & Susa, 2009). There is a possibility that the validation and the verdict could be wrong just like the lie detector (Etcoff, Ekman, Magee, & Frank, 2000). In this case, lie detection procedures have no independent means of checking the phenomena which is lying or confirming whether the accused person lied.


Detecting liars and lies is not an easy task. Even professional lie detectors, like police officers and intelligence officers, fail to do so in most cases. Research shows that professionals also make wrong decisions and fail to distinguish between a lie and truth. One reason behind failing to detect a lie is due to the complexity of the task. There is no single response from a liar that a lie detector can rely on to truly capture a liar. A liar who is determined to lie will avoid being caught at all cost, and there will be an attempt to hide nonverbal, psychological and verbal signs. Liars will try at all cost to create an accurate impression to lie detectors to avoid being caught. They will employ informed tactics to fool whoever is trying to fool them, living the lie detector with mixed feelings about the situation. There are many errors committed by lie detectors that hinder them from knowing the truth; they may tend to focus more on signs that are not linked with the lie. This may be contributed to the fact that they were trained to do so. Some of the techniques the detectors are trained to might be well known by the liars thus making it hard for them to separate the truth from a lie (Vrij, 2008).

Lack of realism is another contributor to the lack of ability to detect lies. Many studies mention that a lie can be detected by observing how people are behaving, screening their speech and analyzing their psychological responses. When lie detectors are conversant with these principles, they are so proud to have the ability to detect deception. Research experts have claimed to have the capacity to detect lies, but they fail to support their study with evidence. Interestingly, lie detectors who have been trained to look for certain cues tend to perform worse than those who do not (Kassin & Fong, 1999; Mann, Vrij, & Bull, 2004).


Lie detection is a science and not a myth since the procedure used in lie detection follows a practical and intellectual activity involving a systematic study of behavior and structure of the natural and physical world using experiment and observation (Moore, Cappelli, Caron, Shaw, Spooner, & Trzeciak, 2011). The process of lie detection, however, could be improved if methods of testing the validity are improved.

While it is important to understand nonverbal, verbal, and physiological indicators of deceit, it is equally important for a lie detector to know which is indicator is more valuable. We sometimes tend to place more emphasis on nonverbal cues when detecting deception, however differences in cultural behaviors may define nonverbal indicators more than lying itself. This leads us to suggest that Emotional Intelligence (EQ), a combination of People Intelligence (PQ) and Cultural Intelligence (CQ), has a key role to play in lie detection.


1.Albrechtsen, J. S., Meissner, C. A., & Susa, K. J. (2009). Can intuition improve deception detection performance? Journal of Experimental Social Psychology , 45, 1052–1055.

2.Ask, K., Granhag, P. A., Juhlin, F., & Vrij, A. (2013). Intending or pretending? Automatic evaluations of goal cues discriminate true and false intentions. Applied Cognitive Psychology , 27, 173–177.

3.Elaad, E. (2003). Effects of feedback on the overestimated capacity to detect lies and the underestimated ability to tell lies. Applied Cognitive Psychology , 17, 349–363.

4.Etcoff, N. L., Ekman, P., Magee, J. J., & Frank, M. G. (2000). Lie detection and language comprehension. Nature , 405, 159.

5.Evans, J. S., & Stanovich, K. E. (2013). Dual-process theories of higher cognition: Advancing the debate. Perspectives on Psychological Science , 8, 223–241.

6.Frank, M. G., & Ekman, P. (2004). Appearing truthful generalizes across different deception situations. Journal of Personality and Social Psychology , 86, 486–495.

7.Frank, M. G., & Feeley, T. H. (2003). To catch a liar: Challenges for research in lie detection training. Journal of Applied Communication Research , 31, 58-75.

8.Kaplar, M. E., & Gordon, A. K. (2004). The enigma of altruistic lying: Perspec- tive differences in what motivates and justifies lie telling within romantic relationships. Personal Relationships , 11, 489–507.

9.Kassin, S. M., & Fong, C. T. (1999). “I’m innocent!”: Effects of training on judgments of truth and deception in the interrogation room. Law and Human Behavior , 23, 499–516.

10.Krauss, R. M. (1981). Impression formation, impression management, and nonverbal behaviors. In E. T. Higgins, C. P. Herman, & M. P. Zanna, Social cognition: The Ontario Symposium (Vol. 1, pp. 323–341). Hillsdale, NJ: Erlbaum.

11.Mann, S., Vrij, A., & Bull, R. (2006). Looking through the eyes of an accurate lie detector. Journal of Credibility Assessment and Witness Psychology , 7 (1–16).

12.Moore, P., Cappelli, D., Caron, T., Shaw, E., Spooner, D., & Trzeciak, R. (2011). A preliminary model of insider theft of intellectual property. Retrieved 2016, from

13.O’Sullivan, M. (2007). Unicorns or Tiger Woods: are lie detection experts myths or rarities? A Response to on lie detection “wizards” by Bond and Uysal. Law Hum Behav , 31 (1), 117-23.

14.Vrij, A. (2008). Detecting Lies and Deceit: Pitfalls and Opportunities (Second ed.). Chichester, West Sussex, England : John Wiley & Sons Ltd.

15.Zuckerman, M., DePaulo, B. M., & Rosenthal, R. (1981). Verbal and non- verbal communication of deception. In L. Berkowitz, Advances inexperimental social psychology. 14, 1–57.

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