Value-driven Security Agreements in Extended Enterprises

Today organizations are highly interconnected in business networks called extended enterprises. This is mostly facilitated by outsourcing and by new economic models based on pay-as-you-go billing; all supported by IT-as-a-service. Although outsourcing has been around for some time, what is now new is the fact that organizations are increasingly outsourcing critical business processes, engaging on complex service bundles, and moving infrastructure and their management to the custody of third parties. Although this gives competitive advantage by reducing cost and increasing flexibility, it increases security risks by eroding security perimeters that used to separate insiders with security privileges from outsiders without security privileges. The classical security distinction between insiders and outsiders is supplemented with a third category of threat agents, namely external insiders, who are not subject to the internal control of an organization but yet have some access privileges to its resources that normal outsiders do not have. Protection against external insiders requires security agreements between organizations in an extended enterprise. Currently, there is no practical method that allows security officers to specify such requirements. In this paper we provide a method for modeling an extended enterprise architecture, identifying external insider roles, and for specifying security requirements that mitigate security threats posed by these roles. We illustrate our method with a realistic example

How secure is ERTMS?

This paper reports on the results of a security analysis of the European Railway Traffic Management System (ERTMS) specifications. ERTMS is designed to be fail-safe and the general philosophy of ‘if in doubt, stop the train’ makes it difficult to engineer a train accident. However, it is possible to exploit the fail-safe behaviour of ERTMS and create a situation that causes a train to halt. Thus, denial of service attacks are possible, and could be launched at a time and place of the attacker’s choosing, perhaps designed to cause maximum disruption or passenger discomfort. Causing an accident is more difficult but not impossible

An Insider Misuse Threat Detection and Prediction Language

Numerous studies indicate that amongst the various types of security threats, the problem of insider misuse of IT systems can have serious consequences for the health of computing infrastructures. Although incidents of external origin are also dangerous, the insider IT misuse problem is difficult to address for a number of reasons. A fundamental reason that makes the problem mitigation difficult relates to the level of trust legitimate users possess inside the organization. The trust factor makes it difficult to detect threats originating from the actions and credentials of individual users. An equally important difficulty in the process of mitigating insider IT threats is based on the variability of the problem. The nature of Insider IT misuse varies amongst organizations. Hence, the problem of expressing what constitutes a threat, as well as the process of detecting and predicting it are non trivial tasks that add up to the multi- factorial nature of insider IT misuse. This thesis is concerned with the process of systematizing the specification of insider threats, focusing on their system-level detection and prediction. The design of suitable user audit mechanisms and semantics form a Domain Specific Language to detect and predict insider misuse incidents. As a result, the thesis proposes in detail ways to construct standardized descriptions (signatures) of insider threat incidents, as means of aiding researchers and IT system experts mitigate the problem of insider IT misuse. The produced audit engine (LUARM – Logging User Actions in Relational Mode) and the Insider Threat Prediction and Specification Language (ITPSL) are two utilities that can be added to the IT insider misuse mitigation arsenal. LUARM is a novel audit engine designed specifically to address the needs of monitoring insider actions. These needs cannot be met by traditional open source audit utilities. ITPSL is an XML based markup that can standardize the description of incidents and threats and thus make use of the LUARM audit data. Its novelty lies on the fact that it can be used to detect as well as predict instances of threats, a task that has not been achieved to this date by a domain specific language to address threats. The research project evaluated the produced language using a cyber-misuse experiment approach derived from real world misuse incident data. The results of the experiment showed that the ITPSL and its associated audit engine LUARM provide a good foundation for insider threat specification and prediction. Some language deficiencies relate to the fact that the insider threat specification process requires a good knowledge of the software applications used in a computer system. As the language is easily expandable, future developments to improve the language towards this direction are suggested

Methodology for a security audit of ERTMS

In this paper we discuss the methodology we used for a security audit of the European Railway Traffic Management System (ERTMS) specifications. ERTMS is a major industrial project that aims at replacing the many different national train control and command systems in Europe. We discuss the stages of the audit, threat model used, and the output of each stage of the audit

A systematic literature review on insider threats

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

Formal Mitigation Strategies for the Insider Threat: A Security Model and Risk Analysis Framework

The advancement of technology and reliance on information systems have fostered an environment of sharing and trust. The rapid growth and dependence on these systems, however, creates an increased risk associated with the insider threat. The insider threat is one of the most challenging problems facing the security of information systems because the insider already has capabilities within the system. Despite research efforts to prevent and detect insiders, organizations remain susceptible to this threat because of inadequate security policies and a willingness of some individuals to betray their organization. To investigate these issues, a formal security model and risk analysis framework are used to systematically analyze this threat and develop effective mitigation strategies. This research extends the Schematic Protection Model to produce the first comprehensive security model capable of analyzing the safety of a system against the insider threat. The model is used to determine vulnerabilities in security policies and system implementation. Through analysis, mitigation strategies that effectively reduce the threat are identified. Furthermore, an action-based taxonomy that expresses the insider threat through measurable and definable actions is presented. A risk analysis framework is also developed that identifies individuals within an organization that display characteristics indicative of a malicious insider. The framework uses a multidisciplinary process by combining behavior and technical attributes to produce a single threat level for each individual within the organization. Statistical analysis using the t-distribution and prediction interval on the threat levels reveal those individuals that are a potential threat to the organization. The effectiveness of the framework is illustrated using the case study of Robert Hanssen, demonstrating the process would likely have identified him as an insider threat

Risk Assessment Framework for Evaluation of Cybersecurity Threats and Vulnerabilities in Medical Devices

Medical devices are vulnerable to cybersecurity exploitation and, while they can provide improvements to clinical care, they can put healthcare organizations and their patients at risk of adverse impacts. Evidence has shown that the proliferation of devices on medical networks present cybersecurity challenges for healthcare organizations due to their lack of built-in cybersecurity controls and the inability for organizations to implement security controls on them. The negative impacts of cybersecurity exploitation in healthcare can include the loss of patient confidentiality, risk to patient safety, negative financial consequences for the organization, and loss of business reputation. Assessing the risk of vulnerabilities and threats to medical devices can inform healthcare organizations toward prioritization of resources to reduce risk most effectively. In this research, we build upon a database-driven approach to risk assessment that is based on the elements of threat, vulnerability, asset, and control (TVA-C). We contribute a novel framework for the cybersecurity risk assessment of medical devices. Using a series of papers, we answer questions related to the risk assessment of networked medical devices. We first conducted a case study empirical analysis that determined the scope of security vulnerabilities in a typical computerized medical environment. We then created a cybersecurity risk framework to identify threats and vulnerabilities to medical devices and produce a quantified risk assessment. These results supported actionable decision making at managerial and operational levels of a typical healthcare organization. Finally, we applied the framework using a data set of medical devices received from a partnering healthcare organization. We compare the assessment results of our framework to a commercial risk assessment vulnerability management system used to analyze the same assets. The study also compares our framework results to the NIST Common Vulnerability Scoring System (CVSS) scores related to identified vulnerabilities reported through the Common Vulnerability and Exposure (CVE) program. As a result of these studies, we recognize several contributions to the area of healthcare cybersecurity. To begin with, we provide the first comprehensive vulnerability assessment of a robotic surgical environment, using a da Vinci surgical robot along with its supporting computing assets. This assessment supports the assertion that networked computer environments are at risk of being compromised in healthcare facilities. Next, our framework, known as MedDevRisk, provides a novel method for risk quantification. In addition, our assessment approach uniquely considers the assets that are of value to a medical organization, going beyond the medical device itself. Finally, our incorporation of risk scenarios into the framework represents a novel approach to medical device risk assessment, which was synthesized from other well-known standards. To our knowledge, our research is the first to apply a quantified assessment framework to the problem area of healthcare cybersecurity and medical networked devices. We would conclude that a reduction in the uncertainty about the riskiness of the cybersecurity status of medical devices can be achieved using this framework