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This paper investigates the psychological traits of individuals’ attraction to engaging in hacking behaviors (both ethical and illegal/unethical)upon entering the workforce.We examine the role of the Dark Triad, Opposition to Authority and Thrill-Seeking traits as regards the propensity of an individual to be interested in White Hat, Black Hat,and Grey Hat hacking. A new set of scales were developed to assist in the delineation of the three hat categories. We also developed a scale to measure each subject’s perception of the probability of being apprehended for violating privacy laws. Engaging in criminal activity involves a choice where there are consequences and opportunities, and individuals perceive them differently, but they can be deterred if there is a likelihood of punishment,and the punishment is severe. The results suggest that individuals that are White Hat, Grey Hat and Black Hat hackers score high on the Machiavellian and Psychopathy scales. We also found evidence that Grey Hatters oppose authority, Black Hatters score high on the thrill-seeking dimension and White Hatters, the good guys, tend to be Narcissists. Thrill-seeking was moderately important for White Hat hacking and Black hat hacking. Opposition to Authority was important for Grey Hat hacking. Narcissism was not statistically significant in any of the models. The probability of being apprehended had a negative effect on Grey Hat and Black Hat hacking. Several suggestions will be made on what organizations can do to address insider threats. 

This paper investigates the psychological traits of individuals’ attraction to engaging in hacking behaviors (both ethical and illegal/unethical)upon entering the workforce.We examine the role of the Dark Triad, Opposition to Authority and Thrill-Seeking traits as regards the propensity of an individual to be interested in White Hat, Black Hat,and Grey Hat hacking. A new set of scales were developed to assist in the delineation of the three hat categories. We also developed a scale to measure each subject’s perception of the probability of being apprehended for violating privacy laws. Engaging in criminal activity involves a choice where there are consequences and opportunities, and individuals perceive them differently, but they can be deterred if there is a likelihood of punishment,and the punishment is severe. The results suggest that individuals that are White Hat, Grey Hat and Black Hat hackers score high on the Machiavellian and Psychopathy scales. We also found evidence that Grey Hatters oppose authority, Black Hatters score high on the thrill-seeking dimension and White Hatters, the good guys, tend to be Narcissists. Thrill-seeking was moderately important for White Hat hacking and Black hat hacking. Opposition to Authority was important for Grey Hat hacking. Narcissism was not statistically significant in any of the models. The probability of being apprehended had a negative effect on Grey Hat and Black Hat hacking. Several suggestions will be made on what organizations can do to address insider threats. 

Deception has been proposed in the literature as an effective defense mechanism to address Advanced Persistent Threats (APT). However, administering deception in a cost-effective manner requires a good understanding of the attack landscape. The attacks mounted by APT groups are highly diverse and sophisticated in nature and can render traditional signature based intrusion detection systems useless. This necessitates the development of behavior oriented defense mechanisms. In this paper, we develop Decepticon (Deception-based countermeasure) a Hidden Markov Model based framework where the indicators of compromise (IoC) are used as the observable features to aid in detection. This framework would help in selecting an appropriate deception script when faced with APTs or other similar malware and trigger an appropriate defensive response. The effectiveness of the model and the associated framework is demonstrated by considering ransomware as the offending APT in a networked system. 

Deception has been proposed in the literature as an effective defense mechanism to address Advanced Persistent Threats (APT). However, administering deception in a cost-effective manner requires a good understanding of the attack landscape. In this paper, we develop a Hidden Markov Model based framework where the indicators of compromise (IoC) are used as the observables. This framework would help in selecting an appropriate deception script and triggering the proper defensive strategy when faced with APTs or other malware. The effectiveness of the model and the associated framework are illustrated by considering ransomware as the offending APT in a networked system.