Towards a Conceptual Model and Reasoning Structure for Insider Threat Detection (original) (raw)
Related papers
ACM Computing Surveys (CSUR), 2018
Insider threats are one of today's most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. Despite several scientific works published in this domain, we argue that the field can benefit from the proposed structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research, while leveraging existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of attackers, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers' efforts in the domain of insider threat, because it provides: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an updated overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders' behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
Insider Threat Assessment: a Model-Based Methodology
Security is a major challenge for today's companies, especially ICT ones which manage large scale cyber-critical systems. Amongst the multitude of attacks and threats to which a system is potentially exposed, there are insider attackers i.e., users with legitimate access which abuse or misuse of their power, thus leading to unexpected security violation (e.g., acquire and disseminate sensitive information). These attacks are very difficult to detect and mitigate due to the nature of the attackers, which often are company's employees motivated by socio-economical reasons, and to the fact that attackers operate within their granted restrictions. It is a consequence that insider attackers constitute an actual threat for ICT organizations. In this paper we present our methodology, together with the application of existing supporting libraries and tools from the state-of-the-art, for insider threats assessment and mitigation. The ultimate objective is to define the motivations and the target of an insider, investigate the likeliness and severity of potential violations, and finally identify appropriate countermeasures. The methodology also includes a maintenance phase during which the assessment can be updated to reflect system changes. As case study, we apply our methodology to the crisis management system Secure!, which includes different kinds of users and consequently is potentially exposed to a large set of insider threats. Keywords security; insider threats; risk assessment; attack path.
Empirical Detection Techniques of Insider Threat Incidents
IEEE Access, 2020
Vital organizations have faced increasing challenges of how to defend against insider threats that may cause a severe damage to their assets. The nature of insider threats is more challenging than external threats, as insiders have a privileged access to sensitive assets of an organization. In fact, there are several studies that reviewed the insider threat detection approaches from taxonomical and theoretical perspectives. However, the protection against insider threat incidents requires empirical defense solutions. Hence, our study uniquely focuses on empirical detection approaches that are validated with empirical results. We propose a 10-question model that highlights different prospective of empirical detection approaches. Significant factors are also proposed to reveal the extent to which the detection approaches are effective against insider threat incidents (e.g., feature domains, protection coverage, classification techniques, simulated scenarios, performance and accuracy metrics, etc.). The objective of this paper is to enhance researchers' efforts in the domain of insider attack by systemizing the detection techniques in comparable manner. It also highlights the challenges and gaps for further research to institute more effective solutions that can predict, detect, and prevent emerging attack incidents. Some recommendations for future research directions are also presented.
Understanding Insider Threat: A Framework for Characterising Attacks
2014 IEEE Security and Privacy Workshops, 2014
The threat that insiders pose to businesses, institutions and governmental organisations continues to be of serious concern. Recent industry surveys and academic literature provide unequivocal evidence to support the significance of this threat and its prevalence. Despite this, however, there is still no unifying framework to fully characterise insider attacks and to facilitate an understanding of the problem, its many components and how they all fit together. In this paper, we focus on this challenge and put forward a grounded framework for understanding and reflecting on the threat that insiders pose. Specifically, we propose a novel conceptualisation that is heavily grounded in insiderthreat case studies, existing literature and relevant psychological theory. The framework identifies several key elements within the problem space, concentrating not only on noteworthy events and indicators-technical and behavioural-of potential attacks, but also on attackers (e.g., the motivation behind malicious threats and the human factors related to unintentional ones), and on the range of attacks being witnessed. The real value of our framework is in its emphasis on bringing together and defining clearly the various aspects of insider threat, all based on realworld cases and pertinent literature. This can therefore act as a platform for general understanding of the threat, and also for reflection, modelling past attacks and looking for useful patterns.
A methodology and supporting techniques for the quantitative assessment of insider threats
Proceedings of the 2nd International Workshop on Dependability Issues in Cloud Computing - DISCCO '13, 2013
Security is a major challenge for today's companies, especially ICT ones which manages large scale cyber-critical systems. Amongst the multitude of attacks and threats to which a system is potentially exposed, there are insiders attackers i.e., users with legitimate access which abuse or misuse of their power, thus leading to unexpected security violation (e.g., acquire and disseminate sensitive information). These attacks are very difficult to detect and mitigate due to the nature of the attackers, which often are company's employees motivated by socio-economical reasons, and to the fact that attackers operate within their granted restrictions: it is a consequence that insiders attackers constitute an actual threat for ICT organizations. In this paper we present our ongoing work towards a methodology and supporting libraries and tools for insider threats assessment and mitigation. The ultimate objective is to quantitatively evaluate the possibility that a user will perform an attack, the severity of potential violations, the costs, and finally select the countermeasures. The methodology also includes a maintenance phase during which the assessment is updated on the basis of system evolution. The paper discusses future works towards the completion of our methodology.
An Insider Threat Prediction Model
Lecture Notes in Computer Science, 2010
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
ACM Computing Surveys, 2019
Insider threats are one of today's most challenging cybersecurity issues that are not well addressed by commonly employed security solutions. In this work we propose structural taxonomy and novel categorization of research that contribute to the organization and disambiguation of insider threat incidents and the defense solutions used against them. The objective of our categorization is to systematize knowledge in insider threat research, while using existing grounded theory method for rigorous literature review. The proposed categorization depicts the workflow among particular categories that include: 1) Incidents and datasets, 2) Analysis of incidents, 3) Simulations, and 4) Defense solutions. Special attention is paid to the definitions and taxonomies of the insider threat; we present a structural taxonomy of insider threat incidents, which is based on existing taxonomies and the 5W1H questions of the information gathering problem. Our survey will enhance researchers' efforts in the domain of insider threat, because it provides: a) a novel structural taxonomy that contributes to orthogonal classification of incidents and defining the scope of defense solutions employed against them, b) an overview on publicly available datasets that can be used to test new detection solutions against other works, c) references of existing case studies and frameworks modeling insiders' behaviors for the purpose of reviewing defense solutions or extending their coverage, and d) a discussion of existing trends and further research directions that can be used for reasoning in the insider threat domain.
DTB Project: A Behavioral Model for Detecting Insider Threats
2005
This paper describes the Detection of Threat Behavior (DTB) project, a joint effort being conducted by George Mason University (GMU) and Information Extraction and Transport, Inc. (IET). DTB uses novel approaches for detecting insiders in tightly controlled computing environments. Innovations include a distributed system of dynamically generated document-centric intelligent agents for document control, objectoriented hybrid logic-based and probabilistic modeling to characterize and detect illicit insider behaviors, and automated data collection and data mining of the operational environment to continually learn and update the underlying statistical and probabilistic nature of characteristic behaviors. To evaluate the DTB concept, we are conducting a human subjects experiment, which we will also include in our discussion.
A Risk Management Approach to the “Insider Threat
2010
Recent surveys indicate that the financial impact and operating losses due to insider intrusions are increasing. But these studies often disagree on what constitutes an “insider;” indeed, manydefine it only implicitly. In theory, appropriate selection of, and enforcement of, properly specified security policies should prevent legitimate users from abusing their access to computer systems, information, and other resources. However, even if policies could be expressed precisely, the natural mapping between the natural language expression of a security policy, and the expression of that policyin a form that can be implemented on a computer system or network, createsgaps in enforcement. This paper defines “insider” precisely, in termsof thesegaps, andexploresan access-based modelfor analyzing threats that include those usually termed “insider threats.” This model enables an organization to order its resources based on thebusinessvalue for that resource andof the information it contains. By identifying those users with access to high-value resources, we obtain an ordered list of users who can cause the greatest amount of damage. Concurrently with this, we examine psychological indicators in order to determine which usersareatthe greatestriskofacting inappropriately. We concludebyexamining how to merge this model with one of forensic logging and auditing.