Extraction of Insider Attack Scenarios from a Formal Information System Modeling

International audienceThe early detection of potential threats during the modelling phase of a Secure Information System is required because it favours the design of a robust access control policy and the prevention of malicious behaviours during the system execution. This paper deals with internal attacks which can be made by people inside the organization. Such at- tacks are difficult to find because insiders have authorized system access and also may be familiar with system policies and procedures. We are in- terested in finding attacks which conform to the access control policy, but lead to unwanted states. These attacks are favoured by policies involving authorization constraints, which grant or deny access depending on the evolution of the functional Information System state. In this context, we propose to model functional requirements and their Role Based Access Control (RBAC) policies using B machines and then to formally reason on both models. In order to extract insider attack scenarios from these B specifications our approach first investigates symbolic behaviours. The use of a model-checking tool allows to exhibit, from a symbolic behaviour, an observable concrete sequence of operations that can be followed by an attacker. In this paper, we show how this combination of symbolic execution and model-checking allows to find out such insider attack sce- narios

Combining a Baiting and a User Search Profiling Techniques for Masquerade Detection

Masquerade attacks are characterized by an adversary stealing a legitimate user's credentials and using them to impersonate the victim and perform malicious activities, such as stealing information. Prior work on masquerade attack detection has focused on profiling legitimate user behavior and detecting abnormal behavior indicative of a masquerade attack. Like any anomaly-detection based techniques, detecting masquerade attacks by profiling user behavior suffers from a significant number of false positives. We extend prior work and provide a novel integrated detection approach in this paper. We combine a user behavior profiling technique with a baiting technique in order to more accurately detect masquerade activity. We show that using this integrated approach reduces the false positives by 36% when compared to user behavior profiling alone, while achieving almost perfect detection results. We also show how this combined detection approach serves as a mechanism for hardening the masquerade attack detector against mimicry attacks

Automated insider threat detection system using user and role-based profile assessment

© 2007-2012 IEEE. 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

Medical Cyber-Physical Systems Development: A Forensics-Driven Approach

The synthesis of technology and the medical industry has partly contributed to the increasing interest in Medical Cyber-Physical Systems (MCPS). While these systems provide benefits to patients and professionals, they also introduce new attack vectors for malicious actors (e.g. financially-and/or criminally-motivated actors). A successful breach involving a MCPS can impact patient data and system availability. The complexity and operating requirements of a MCPS complicates digital investigations. Coupling this information with the potentially vast amounts of information that a MCPS produces and/or has access to is generating discussions on, not only, how to compromise these systems but, more importantly, how to investigate these systems. The paper proposes the integration of forensics principles and concepts into the design and development of a MCPS to strengthen an organization's investigative posture. The framework sets the foundation for future research in the refinement of specific solutions for MCPS investigations.Comment: This is the pre-print version of a paper presented at the 2nd International Workshop on Security, Privacy, and Trustworthiness in Medical Cyber-Physical Systems (MedSPT 2017

Understanding How Components of Organisations Contribute to Attacks

Attacks on organisations today explore many different layers, including buildings infrastructure, IT infrastructure, and human factor – the physical, virtual, and social layer. Identifying possible attacks, understanding their impact, and attributing their origin and contributing factors is difficult. Recently, system models have been used for automatically identifying possible attacks on the modelled organisation. The generated attacks consider all three layers, making the contribution of building infrastructure, computer infrastructure, and humans (insiders and outsiders) explicit. However, this contribution is only visible in the attack trees as part of the performed steps; it cannot be mapped back to the model directly since the actions usually involve several elements (attacker and targeted actor or asset). Especially for large attack trees, understanding the relations between several model components quickly results in a large quantity of interrelations, which are hard to grasp. In this work we present several approaches for visualising attributes of attacks such as likelihood of success, impact, and required time or skill level. The resulting visualisations provide a link between attacks on an organisations and the contribution of parts of an organisation to the attack and its impact

Modelling Socio-Technical Aspects of Organisational Security

Identification of threats to organisations and risk assessment often take into consideration the pure technical aspects, overlooking the vulnerabilities originating from attacks on a social level, for example social engineering, and abstracting away the physical infrastructure. However, attacks on organisations are far from being purely technical. After all, organisations consist of employees. Often the human factor appears to be the weakest point in the security of organisations. It may be easier to break through a system using a social engineering attack rather than a pure technological one. The StuxNet attack is only one of the many examples showing that vulnerabilities of organisations are increasingly exploited on different levels including the human factor. There is an urgent need for integration between the technical and social aspects of systems in assessing their security. Such an integration would close this gap, however, it would also result in complicating the formal treatment and automatic identification of attacks. This dissertation shows that applying a system modelling approach to sociotechnical systems can be used for identifying attacks on organisations, which exploit various levels of the vulnerabilities of the systems. In support of this claim we present a modelling framework, which combines many features. Based on a graph, the framework presents the physical infrastructure of an organisation, where actors and data are modelled as nodes in this graph. Based on the semantics of the underlying process calculus, we develop a formal analytical approach that generates attack trees from the model. The overall goal of the framework is to predict, prioritise and minimise the vulnerabilities in organisations by prohibiting the overall attack or at least increasing the difficulty and cost of fulfilling it. We validate our approach using scenarios from IPTV and Cloud Infrastructure case studies

Insider Threat Detection on the Windows Operating System using Virtual Machine Introspection

Existing insider threat defensive technologies focus on monitoring network traffic or events generated by activities on a user\u27s workstation. This research develops a methodology for signaling potentially malicious insider behavior using virtual machine introspection (VMI). VMI provides a novel means to detect potential malicious insiders because the introspection tools remain transparent and inaccessible to the guest and are extremely difficult to subvert. This research develops a four step methodology for development and validation of malicious insider threat alerting using VMI. Six core use cases are developed along with eighteen supporting scenarios. A malicious attacker taxonomy is used to decompose each scenario to aid identification of observables for monitoring for potentially malicious actions. The effectiveness of the identified observables is validated through the use of two data sets, one containing simulated normal and malicious insider user behavior and the second from a computer network operations exercise. Compiled Memory Analysis Tool - Virtual (CMAT-V) and Xen hypervisor capabilities are leveraged to perform VMI and insider threat detection. Results of the research show the developed methodology is effective in detecting all defined malicious insider scenarios used in this research on Windows guests