612 research outputs found

    Over-claiming as a Predictor of Insider Threat Activities in Individuals

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    Insiders can engage in malicious activities against organizations such as data theft and sabotage. Prior research on insider threat behavior indicates that once motivated to commit malicious activity, insiders seek opportunity where they can act without being detected. In this research we set up an experiment where we leverage this opportunistic behavior and present participants with messages signaling opportunity for data theft. In the experiment, students were engaged in routine tasks with a bonus based on their performance. While working on their assigned tasks, they were presented with opportunities (probes) to steal data that would increase their payout. Their pre and post probe behavior was observed to test if they engaged in behavior that was deemed suspicious when they received the probe. The goal of the project is to test whether the overclaiming personality trait is a predictor of malicious insider behavior and this was measured through the Over Claiming questionnaire developed by Paulhaus (Paulhaus et al. 2003) The results indicated that over claiming proved to be a strong predictor of malicious insider behavior

    Detecting insider threat within institutions using CERT dataset and different ML techniques

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    The reason of countries development in industrial and commercial enterprises fields in those countries. The security of a particular country depends on its security institutions, the confidentiality of its employees, their information, the target's information, and information about the forensic evidence for those targets. One of the most important and critical problems in such institutions is the problem of discovering an insider threat that causes loss, damage, or theft the information to hostile or competing parties. This threat is represented by a person who represents one of the employees of the institution, the goal of that person is to steal information or destroy it for the benefit of another institution's desires. The difficulty in detecting this type of threat is due to the difficulty of analyzing the behavior of people within the organization according to their physiological characteristics. In this research, CERT dataset that produced by the University of Carnegie Mellon University has been used in this investigation to detect insider threat. The dataset has been preprocessed. Five effective features were selected to apply three ML techniques Random Forest, Naïve Bayes, and 1 Nearest Neighbor. The results obtained and listed sequentially as 89.75917519%, 91.96650826%, and 94.68205476% with an error rate of 10.24082481%, 8.03349174%, and 5.317945236%

    A unified classification model to insider threats to information security

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    Prior work on insider threat classification has adopted a range of definitions, constructs, and terminology, making it challenging to compare studies. We address this issue by introducing a unified insider threat classification model built through a comprehensive and systematic review of prior work. An insider threat can be challenging to predict, as insiders may utilise motivation, creativity, and ingenuity. Understanding the different types of threats to information security (and cybersecurity) is crucial as it helps organisations develop the right preventive strategies. This paper presents a thematic analysis of the literature on the types of insider threats to cybersecurity to provide cohesive definitions and consistent terminology of insider threats. We demonstrate that the insider threat exists on a continuum of accidental, negligent, mischievous, and malicious behaviour. The proposed insider threat classification can help organisations to identify, implement, and contribute towards improving their cybersecurity strategies

    Graph Based Framework for Malicious Insider Threat Detection

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    While most security projects have focused on fending off attacks coming from outside the organizational boundaries, a real threat has arisen from the people who are inside those perimeter protections. \ Insider threats have shown their power by hugely affecting national security, financial stability, and the privacy of many thousands of people. What is in the news is the tip of the iceberg, with much more going on under the radar, and some threats never being detected. We propose a hybrid framework based on graphical analysis and anomaly detection approaches, to combat this severe cyber security threat. Our framework analyzes heterogeneous data in isolating possible malicious users hiding behind others. Empirical results reveal this framework to be effective in distinguishing the majority of users who demonstrate typical behavior from the minority of users who show suspicious behavior.

    Novel Alert Visualization: The Development of a Visual Analytics Prototype for Mitigation of Malicious Insider Cyber Threats

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    Cyber insider threat is one of the most difficult risks to mitigate in organizations. However, innovative validated visualizations for cyber analysts to better decipher and react to detected anomalies has not been reported in literature or in industry. Attacks caused by malicious insiders can cause millions of dollars in losses to an organization. Though there have been advances in Intrusion Detection Systems (IDSs) over the last three decades, traditional IDSs do not specialize in anomaly identification caused by insiders. There is also a profuse amount of data being presented to cyber analysts when deciphering big data and reacting to data breach incidents using complex information systems. Information visualization is pertinent to the identification and mitigation of malicious cyber insider threats. The main goal of this study was to develop and validate, using Subject Matter Experts (SME), an executive insider threat dashboard visualization prototype. Using the developed prototype, an experimental study was conducted, which aimed to assess the perceived effectiveness in enhancing the analysts’ interface when complex data correlations are presented to mitigate malicious insiders cyber threats. Dashboard-based visualization techniques could be used to give full visibility of network progress and problems in real-time, especially within complex and stressful environments. For instance, in an Emergency Room (ER), there are four main vital signs used for urgent patient triage. Cybersecurity vital signs can give cyber analysts clear focal points during high severity issues. Pilots must expeditiously reference the Heads Up Display (HUD), which presents only key indicators to make critical decisions during unwarranted deviations or an immediate threat. Current dashboard-based visualization techniques have yet to be fully validated within the field of cybersecurity. This study developed a visualization prototype based on SME input utilizing the Delphi method. SMEs validated the perceived effectiveness of several different types of the developed visualization dashboard. Quantitative analysis of SME’s perceived effectiveness via self-reported value and satisfaction data as well as qualitative analysis of feedback provided during the experiments using the prototype developed were performed. This study identified critical cyber visualization variables and identified visualization techniques. The identifications were then used to develop QUICK.v™ a prototype to be used when mitigating potentially malicious cyber insider threats. The perceived effectiveness of QUICK.v™ was then validated. Insights from this study can aid organizations in enhancing cybersecurity dashboard visualizations by depicting only critical cybersecurity vital signs

    A systematic literature review on insider threats

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    Insider threats is the most concerned cybersecurity problem which is poorly addressed by widely used security solutions. Despite the fact that there have been several scientific publications in this area, but from our innovative study classification and structural taxonomy proposals, we argue to provide the more information about insider threats and defense measures used to counter them. While adopting the current grounded theory method for a thorough literature evaluation, our categorization's goal is to organize knowledge in insider threat research. Along with an analysis of major recent studies on detecting insider threats, the major goal of the study is to develop a classification of current types of insiders, levels of access, motivations behind it, insider profiling, security properties, and methods they use to attack. This includes use of machine learning algorithm, behavior analysis, methods of detection and evaluation. Moreover, actual incidents related to insider attacks have also been analyzed

    How Often Is Employee Anger An Insider Risk II? Detecting and Measuring Negative Sentiment versus Insider Risk in Digital Communications–Comparison between Human Raters and Psycholinguistic Software

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    This research uses two recently introduced observer rating scales, (Shaw et al., 2013) for the identification and measurement of negative sentiment (the Scale for Negativity in Text or SNIT) and insider risk (Scale of Indicators of Risk in Digital Communication or SIRDC) in communications to test the performance of psycholinguistic software designed to detect indicators of these risk factors. The psycholinguistic software program, WarmTouch (WT), previously used for investigations, appeared to be an effective means for locating communications scored High or Medium in negative sentiment by the SNIT or High in insider risk by the SIRDC within a randomly selected sample from the Enron archive. WT proved less effective in locating emails Low in negative sentiment on the SNIT and Low in insider risk on the SIRDC. However, WT performed extremely well in identifying communications from actual insiders randomly selected from case files and inserted in this email sample. In addition, it appeared that WT’s measure of perceived Victimization was a significant supplement to using negative sentiment alone, when it came to searching for actual insiders. Previous findings ( Shaw et al., 2013) indicate that this relative weakness in identifying low levels of negative sentiment may not impair WT’s usefulness for identifying communications containing significant indications of insider risk because of the very low base rate and low severity of insider risk at Low levels of negative sentiment (Shaw et al., 2013). Although many of the “false positives” acquired in the successful search for actual insiders in this experiment were shown to be true positives for other forms of insider risk, WT still produced fairly high rates of false positives that could burden analysts, as described by the search times provided. As further research and development proceeds to address this problem, we again recommend the use of WT in an integrated multi-disciplinary array of detection methods that will serve as an initial screen to narrow the search for individuals at-risk for insider activities. The implications for insider threat research, detection and prevention are discussed
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