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Services Computing for Cyber-Threat Intelligence: The ANITA Approach
Abstract
Major cybersecurity and threat intelligence analysts agree that online criminal activity is increasing exponentially. Technologies, newspapers, the internet, and social media made the dark web an accessible place to almost everyone. The ease of accessing the dark side of the web makes the problem more critical than ever. For this reason, the European Union financed the ANITA project, consisting of different tools for monitoring and fighting illegal criminal activities on the Dark Web. In the ANITA project, we propose different Big Data analytic tools for the analysis of all data extracted from illegal marketplaces. In this survey paper we present our developed tools for detecting trends and analyzing the incoming information with respect to illegal trafficking. The tool extracts information about specific trends, analytics and produces actionable insight on buying and transaction habits and user behaviors. The tool extracts statistics in order to support and guide investigators and law enforcement agencies for the detection of criminal activities.
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Extant research indicates that professional law enforcement officers (LEOs) are generally no better than untrained novices at detecting deception. Moreover, traditional training methods are often less effective than no training at all at improving successful detection. Compared to the traditional training, interactive digital games can provide an immersive learning environment for deeper internalization of new information through simulated practices. VERITAS—an interactive digital game—was designed and developed to train LEOs to better detect reliable deception cues when questioning suspects and determining the veracity of their answers. The authors hypothesized that reducing players' reactance would mitigate resistance to training, motivate engagement with materials, and result in greater success at deception detection and knowledge. As hypothesized, LEOs playing VERITAS showed significant improvement in deception detection from the first to the second scenario within the game; and the low-reactance version provided the most effective training. The authors also compared various responses to the game between LEOs and a separate undergraduate student sample. Relative to students, findings show LEOs perceived VERITAS to be significantly more intrinsically motivating, engaging, and appealing as a deception detection activity.
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