Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD

Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by ‘open networks’, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings

Attack-Surface Metrics, OSSTMM and Common Criteria Based Approach to “Composable Security” in Complex Systems

In recent studies on Complex Systems and Systems-of-Systems theory, a huge effort has been put to cope with behavioral problems, i.e. the possibility of controlling a desired overall or end-to-end behavior by acting on the individual elements that constitute the system itself. This problem is particularly important in the “SMART” environments, where the huge number of devices, their significant computational capabilities as well as their tight interconnection produce a complex architecture for which it is difficult to predict (and control) a desired behavior; furthermore, if the scenario is allowed to dynamically evolve through the modification of both topology and subsystems composition, then the control problem becomes a real challenge. In this perspective, the purpose of this paper is to cope with a specific class of control problems in complex systems, the “composability of security functionalities”, recently introduced by the European Funded research through the pSHIELD and nSHIELD projects (ARTEMIS-JU programme). In a nutshell, the objective of this research is to define a control framework that, given a target security level for a specific application scenario, is able to i) discover the system elements, ii) quantify the security level of each element as well as its contribution to the security of the overall system, and iii) compute the control action to be applied on such elements to reach the security target. The main innovations proposed by the authors are: i) the definition of a comprehensive methodology to quantify the security of a generic system independently from the technology and the environment and ii) the integration of the derived metrics into a closed-loop scheme that allows real-time control of the system. The solution described in this work moves from the proof-of-concepts performed in the early phase of the pSHIELD research and enrich es it through an innovative metric with a sound foundation, able to potentially cope with any kind of pplication scenarios (railways, automotive, manufacturing, ...)

A decision support system for corporations cyber security risk management

This thesis presents a decision aiding system named C3-SEC (Contex-aware Corporative Cyber Security), developed in the context of a master program at Polytechnic Institute of Leiria, Portugal. The research dimension and the corresponding software development process that followed are presented and validated with an application scenario and case study performed at Universidad de las Fuerzas Armadas ESPE – Ecuador. C3-SEC is a decision aiding software intended to support cyber risks and cyber threats analysis of a corporative information and communications technological infrastructure. The resulting software product will help corporations Chief Information Security Officers (CISO) on cyber security risk analysis, decision-making and prevention measures for the infrastructure and information assets protection. The work is initially focused on the evaluation of the most popular and relevant tools available for risk assessment and decision making in the cyber security domain. Their properties, metrics and strategies are studied and their support for cyber security risk analysis, decision-making and prevention is assessed for the protection of organization's information assets. A contribution for cyber security experts decision support is then proposed by the means of reuse and integration of existing tools and C3-SEC software. C3-SEC extends existing tools features from the data collection and data analysis (perception) level to a full context-ware reference model. The software developed makes use of semantic level, ontology-based knowledge representation and inference supported by widely adopted standards, as well as cyber security standards (CVE, CPE, CVSS, etc.) and cyber security information data sources made available by international authorities, to share and exchange information in this domain. C3-SEC development follows a context-aware systems reference model addressing the perception, comprehension, projection and decision/action layers to create corporative scale cyber security situation awareness

Cyber-security Risk Assessment

Cyber-security domain is inherently dynamic. Not only does system configuration changes frequently (with new releases and patches), but also new attacks and vulnerabilities are regularly discovered. The threat in cyber-security is human, and hence intelligent in nature. The attacker adapts to the situation, target environment, and countermeasures. Attack actions are also driven by attacker's exploratory nature, thought process, motivation, strategy, and preferences. Current security risk assessment is driven by cyber-security expert's theories about this attacker behavior. The goal of this dissertation is to automatically generate the cyber-security risk scenarios by: * Capturing diverse and dispersed cyber-security knowledge * Assuming that there are unknowns in the cyber-security domain, and new knowledge is available frequently * Emulating the attacker's exploratory nature, thought process, motivation, strategy, preferences and his/her interaction with the target environment * Using the cyber-security expert's theories about attacker behavior The proposed framework is designed by using the unique cyber-security domain requirements identified in this dissertation and by overcoming the limitations of current risk scenario generation frameworks. The proposed framework automates the risk scenario generation by using the knowledge as it becomes available (or changes). It supports observing, encoding, validating, and calibrating cyber-security expert's theories. It can also be used for assisting the red-teaming process. The proposed framework generates ranked attack trees and encodes the attacker behavior theories. These can be used for prioritizing vulnerability remediation. The proposed framework is currently being extended for developing an automated threat response framework that can be used to analyze and recommend countermeasures. This framework contains behavior driven countermeasures that uses the attacker behavior theories to lead the attacker away from the system to be protected

Vulnerability prediction for secure healthcare supply chain service delivery

Healthcare organisations are constantly facing sophisticated cyberattacks due to the sensitivity and criticality of patient health care information and wide connectivity of medical devices. Such attacks can pose potential disruptions to critical services delivery. There are number of existing works that focus on using Machine Learning(ML) models for pre-dicting vulnerability and exploitation but most of these works focused on parameterized values to predict severity and exploitability. This paper proposes a novel method that uses ontology axioms to define essential concepts related to the overall healthcare ecosystem and to ensure semantic consistency checking among such concepts. The application of on-tology enables the formal specification and description of healthcare ecosystem and the key elements used in vulnerabil-ity assessment as a set of concepts. Such specification also strengthens the relationships that exist between healthcare-based and vulnerability assessment concepts, in addition to semantic definition and reasoning of the concepts. Our work also makes use of Machine Learning techniques to predict possible security vulnerabilities in health care supply chain services. The paper demonstrates the applicability of our work by using vulnerability datasets to predict the exploitation. The results show that the conceptualization of healthcare sector cybersecurity using an ontological approach provides mechanisms to better understand the correlation between the healthcare sector and the security domain, while the ML algorithms increase the accuracy of the vulnerability exploitability prediction. Our result shows that using Linear Regres-sion, Decision Tree and Random Forest provided a reasonable result for predicting vulnerability exploitability

A Comparison of Cybersecurity Risk Analysis Tools

This paper presents the ongoing work of a decision aiding software intended to support cyber risk and cyber threats analysis of an information and communications technology infrastructure. The work focuses on the evaluation of the different tools in relation to risk assessment and decision making to incorporate some of the characteristics, metrics and strategies that will help cybersecurity risk analysis, decision-making, prevention measures and risk strategies for infrastructure and the protection of an organization's information assets

A Relevance Model for Threat-Centric Ranking of Cybersecurity Vulnerabilities

The relentless and often haphazard process of tracking and remediating vulnerabilities is a top concern for cybersecurity professionals. The key challenge they face is trying to identify a remediation scheme specific to in-house, organizational objectives. Without a strategy, the result is a patchwork of fixes applied to a tide of vulnerabilities, any one of which could be the single point of failure in an otherwise formidable defense. This means one of the biggest challenges in vulnerability management relates to prioritization. Given that so few vulnerabilities are a focus of real-world attacks, a practical remediation strategy is to identify vulnerabilities likely to be exploited and focus efforts towards remediating those vulnerabilities first. The goal of this research is to demonstrate that aggregating and synthesizing readily accessible, public data sources to provide personalized, automated recommendations that an organization can use to prioritize its vulnerability management strategy will offer significant improvements over what is currently realized using the Common Vulnerability Scoring System (CVSS). We provide a framework for vulnerability management specifically focused on mitigating threats using adversary criteria derived from MITRE ATT&CK. We identify the data mining steps needed to acquire, standardize, and integrate publicly available cyber intelligence data sets into a robust knowledge graph from which stakeholders can infer business logic related to known threats. We tested our approach by identifying vulnerabilities in academic and common software associated with six universities and four government facilities. Ranking policy performance was measured using the Normalized Discounted Cumulative Gain (nDCG). Our results show an average 71.5% to 91.3% improvement towards the identification of vulnerabilities likely to be targeted and exploited by cyber threat actors. The ROI of patching using our policies resulted in a savings in the range of 23.3% to 25.5% in annualized unit costs. Our results demonstrate the efficiency of creating knowledge graphs to link large data sets to facilitate semantic queries and create data-driven, flexible ranking policies. Additionally, our framework uses only open standards, making implementation and improvement feasible for cyber practitioners and academia

DETERMINING VULNERABILITY USING ATTACK GRAPHS: AN EXPANSION OF THE CURRENT FAIR MODEL

Factor Analysis of Information Risk (FAIR) provides a framework for measuring and understanding factors that contribute to information risk. One such factor is FAIR Vulnerability; the probability that an event involving a threat will result in a loss. An asset is vulnerable if a threat actor’s Threat Capability is higher than the Resistance Strength of the asset. In FAIR scenarios, Resistance Strength is currently estimated for entire assets, oversimplifying assets containing individual systems and the surrounding environment. This research explores enhancing estimations of FAIR Vulnerability by modeling interactions between threat actors and assets through attack graphs. By breaking down the scenario into more representative and quantifiable parts, more detailed and precise analyses are possible