Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> Information systems face several security threats, some of which originate by insiders. This paper presents a novel, interdisciplinary insider threat prediction model. It combines approaches, techniques, and tools from computer science and psychology. It utilizes real time monitoring, capturing the user's technological trait in an information system and analyzing it for misbehavior. In parallel, the model is using data from psychometric tests, so as to assess for each user the predisposition to malicious acts and the stress level, which is an enabler for the user to overcome his moral inhibitions, under the condition that the collection of such data complies with the legal framework. The model combines the above mentioned information, categorizes users, and identifies those that require additional monitoring, as they can potentially be dangerous for the information system and the organization. <s> BIB001 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> This paper introduces a Bayesian network model for the motivation and psychology of the malicious insider. First, an initial model was developed based on results in the research literature, highlighting critical variables for the prediction of degree of interest in a potentially malicious insider. Second, a survey was conducted to measure these predictive variables in a common sample of normal participants. Third, a structural equation model was constructed based on the original model, updated based on a split-half sample of the empirical survey data and validated against the other half of the dataset. Fourth, the Bayesian network was adjusted in light of the results of the empirical analysis. Fifth, the updated model was used to develop an upper bound on the quality of model predictions of its own simulated data. When empirical data regarding psychological predictors were input to the model, predictions of counterproductive behavior approached the upper bound of model predictiveness. <s> BIB002 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> The act of conducting an insider attack carries with it cognitive and social challenges that may affect an offender’s day-to-day work behavior. We test this hypothesis by examining the language used in e-mails that were sent as part of a 6-hr workplace simulation. The simulation involved participants (N = 54) examining databases and exchanging information as part of a four-stage organized crime investigation. After the first stage, 25% of the participants were covertly incentivized to act as an “insider” by providing information to a provocateur. Analysis of the language used in participants’ e-mails found that insiders became more self-focused, showed greater negative affect, and showed more cognitive processing compared to their coworkers. At the interpersonal level, insiders showed significantly more deterioration in the degree to which their language mimicked other team members over time. Our findings demonstrate how language may provide an indirect way of identifying employees who are undertaking an insider attack. <s> BIB003 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> Social media have widened society’s opportunities for communication, while they offer ways to perform employees’ screening and profiling. Our goal in this paper is to develop an insider threat prediction method by (e)valuating a users’ personality trait of narcissism, which is deemed to be closely connected to the manifestation of malevolent insiders. We utilize graph theory tools in order to detect influence of and usage deviation. Then, we categorize the users according to a proposed taxonomy. Thus we detect individuals with narcissistic characteristics and manage to test groups of people under the prism of group homogeneity. Furthermore, we compare and classify users to larger sub-communities consisting of people of the same profession. The analysis is based on an extensive crawling of Greek users of Twitter. As the application of this method may lead to infringement of privacy rights, its use should be reserved for exceptional cases, such as the selection of security officers or of critical infrastructures decision-making staff. <s> BIB004 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> Addressing the insider threat is a major issue in cyber and corporate security in order to enhance trusted computing in critical infrastructures. In this paper we study the psychosocial perspective and the implications of insider threat prediction via social media, Open Source Intelligence and user generated content classification. Inductively, we propose a prediction method by evaluating the predisposition towards law enforcement and authorities, a personal psychosocial trait closely connected to the manifestation of malevolent insiders. We propose a methodology to detect users holding negative attitude towards authorities. For doing so, we facilitate a brief analysis of the medium (YouTube), machine learning techniques and a dictionary-based approach, in order to detect comments expressing negative attitude. Thus, we can draw conclusions over a user behavior and beliefs via the content the user generated within the limits a social medium. We also use an assumption free flat data representation technique in order to decide over the user's attitude and improve the scalability of our method. Furthermore, we compare the results of each method and highlight the common behavior and characteristics manifested by the users. As privacy violations may well-rise when using such methods, their use should be restricted only on exceptional cases, e.g. when appointing security officers or decision-making staff in critical infrastructures. <s> BIB005 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> Insider threats detection problem has always been one of the most difficult challenges for organizations and research community. Effective behavioral categorization of users plays a vital role for the success of any detection mechanisms. It also helps to reduce false alarms in case of insider threats. In order to achieve this, a fuzzy classifier has been implemented along with genetic algorithm (GA) to enhance the efficiency of a fuzzy classifier. It also enhances the functionality of all other modules to achieve better results in terms of false alarms. A scenario driven approach along with mathematical evaluation verifies the effectiveness of the modified framework. It has been tested for the enterprises having critical nature of business. Other organizations can adopt it in accordance with their specific nature of business, need, and operational processes. The results prove that accurate classification and detection of users were achieved by adopting the modified framework which in turn minimizes false alarms. <s> BIB006 </s> Detection and prediction of insider threats to cyber security: a systematic literature review and meta-analysis <s> a) Motive <s> Organizations are experiencing an ever-growing concern of how to identify and defend against insider threats. Those who have authorized access to sensitive organizational data are placed in a position of power that could well be abused and could cause significant damage to an organization. This could range from financial theft and intellectual property theft to the destruction of property and business reputation. Traditional intrusion detection systems are neither designed nor capable of identifying those who act maliciously within an organization. In this paper, we describe an automated system that is capable of detecting insider threats within an organization. We define a tree-structure profiling approach that incorporates the details of activities conducted by each user and each job role and then use this to obtain a consistent representation of features that provide a rich description of the user's behavior. Deviation can be assessed based on the amount of variance that each user exhibits across multiple attributes, compared against their peers. We have performed experimentation using ten synthetic data-driven scenarios and found that the system can identify anomalous behavior that may be indicative of a potential threat. We also show how our detection system can be combined with visual analytics tools to support further investigation by an analyst. <s> BIB007
The motive refers to the reason or cause why an insider or group of insiders will perpetrate a crime. Previous studies have grouped the features associated with motives into four broad categories BIB006 BIB007 BIB002 BIB003 BIB004 BIB001 BIB005 : i. Predisposition to malicious behaviour-Honeypots are made to capture the predisposition of the insider towards malicious activities. A honeypot is a trap set to identify malicious insiders. Any interaction with the honeypot is considered abnormal. The usage of honeypots in network measures the probability that an insider will attack against the system if an opportunity arises. ii. Mental disorders (e.g., paranoia, depression, hearing voices etc.), iii. Personality factors (e.g., narcissism, neuroticism, sociopathy etc.) and iv. Current emotional state (e.g., hostility, stress, anger etc.) Table 1 displays the features that belong to these four categories and the sources from which the values of these features are obtained.