Efforts to understand what goes on in the mind of an insider have taken a back seat to developing technical controls, yet insider threat incidents persist. We examine insider threat incidents with malicious intent and propose an explanation through a relationship between Dark Triad personality traits and the insider threat. Although Dark Triad personality traits have emerged in insider threat cases and deviant workplace behavior studies, they have not been labeled as such and little empirical research has examined this phenomenon. This paper builds on previous research on insider threat and introduces ten propositions concerning the relationship between Dark Triad personality traits and insider threat behavior. We include behavioral antecedents based on the Theory of Planned Behavior andCapability Means Opportunity (CMO) model and the factors affecting those antecedents. This research addresses the behavioral aspect of the insider threat and provides new information in support of academics and practitioners.
Digital forensics is a relatively new scientific discipline, but one that has matured greatly over the past decade. In any field of human endeavor, it is important to periodically pause and review the state of the discipline. This paper examines where the discipline of digital forensics is at this It is a compilation of the author's opinion and the viewpoints of twenty-one other practitioners and researchers, many of whom are leaders in the field. In synthesizing these professional opinions, several consensus views emerge that provide valuable insights into the "state of the discipline."
Investigators and analysts are increasingly experiencing large, even terabyte sized data sets when conducting digital investigations. Stateof-the-art digital investigation tools and processes are efficiency constrained from both system and human perspectives, due to their continued reliance on overly simplistic data reduction and mining algorithms. The extension of data mining research to the digital forensic science discipline will have some or all of the following benefits: (i) reduced system and human processing time associated with data analysis; (ii) improved information quality associated with data analysis; and (iii) reduced monetary costs associated with digital investigations. This paper introduces data mining and reviews the limited extant literature pertaining to the application of data mining to digital investigations and forensics. Finally, it provides suggestions for applying data mining research to digital forensics.
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