Biblio

A methodology for studying the level of security for various types of CPS through the analysis of the consequences was developed during the research process. An analysis of the architecture of cyber-physical systems was carried out, vulnerabilities and threats of specific devices were identified, a list of possible information attacks and their consequences after the exploitation of vulnerabilities was identified. The object of research is models of cyber-physical systems, including IoT devices, microcomputers, various sensors that function through communication channels, organized by cyber-physical objects. The main subjects of this investigation are methods and means of security testing of cyber-physical systems (CPS). The main objective of this investigation is to update the problem of security in cyber-physical systems, to analyze the security of these systems. In practice, the testing methodology for the cyber-physical system “Smart Factory” was implemented, which simulates the operation of a real CPS, with different types of links and protocols used.

In this paper, we propose a graph-based algorithmic technique for malware detection, utilizing the System-call Dependency Graphs (ScDG) obtained through taint analysis traces. We leverage the grouping of system-calls into system-call groups with respect to their functionality to merge disjoint vertices of ScDG graphs, transforming them to Group Relation Graphs (GrG); note that, the GrG graphs represent malware's behavior being hence more resilient to probable mutations of its structure. More precisely, we extend the use of GrG graphs by mapping their vertices on the plane utilizing the degrees and the vertex-weights of a specific underlying graph of the GrG graph as to compute domination relations. Furthermore, we investigate how the activity of each system-call group could be utilized in order to distinguish graph-representations of malware and benign software. The domination relations among the vertices of GrG graphs result to a new graph representation that we call Coverage Graph of the GrG graph. Finally, we evaluate the potentials of our detection model using graph similarity between Coverage Graphs of known malicious and benign software samples of various types.

In a world where traditional notions of privacy are increasingly challenged by the myriad companies that collect and analyze our data, it is important that decision-making entities are held accountable for unfair treatments arising from irresponsible data usage. Unfortunately, a lack of appropriate methodologies and tools means that even identifying unfair or discriminatory effects can be a challenge in practice. We introduce the unwarranted associations (UA) framework, a principled methodology for the discovery of unfair, discriminatory, or offensive user treatment in data-driven applications. The UA framework unifies and rationalizes a number of prior attempts at formalizing algorithmic fairness. It uniquely combines multiple investigative primitives and fairness metrics with broad applicability, granular exploration of unfair treatment in user subgroups, and incorporation of natural notions of utility that may account for observed disparities. We instantiate the UA framework in FairTest, the first comprehensive tool that helps developers check data-driven applications for unfair user treatment. It enables scalable and statistically rigorous investigation of associations between application outcomes (such as prices or premiums) and sensitive user attributes (such as race or gender). Furthermore, FairTest provides debugging capabilities that let programmers rule out potential confounders for observed unfair effects. We report on use of FairTest to investigate and in some cases address disparate impact, offensive labeling, and uneven rates of algorithmic error in four data-driven applications. As examples, our results reveal subtle biases against older populations in the distribution of error in a predictive health application and offensive racial labeling in an image tagger.

This brief paper reports on an early stage ongoing PhD project in the field of cyber-physical security in health care critical infrastructures. The research overall aims to develop a methodology that will increase the ability of secure recovery of health critical infrastructures. This ambitious or reckless attempt, as it is currently at an early stage, in this paper, tries to answer why cyber-physical security for health care infrastructures is important and of scientific interest. An initial PhD project methodology and expected outcomes are also discussed. The report concludes with challenges that emerge and possible future directions.

Abstract. Multi-agent cyber-physical systems (CPSs) are ubiquitous in modern infrastructure systems, including the future smart grid, transportation networks, and public health systems. Security of these systems are critical for normal operation of our society. In this paper, we focus on physical layer resilient control of these systems subject to cyber attacks and malicious behaviors of physical agents. We establish a cross-layer system model for the investigation of cross-layer coupling and performance interdependencies for CPSs. In addition, we study a twosystem synchronization problem in which one is a malicious agent who intends to mislead the entire system behavior through physical layer interactions. Feedback Nash equilibrium is used as the solution concept for the distributed control in the multi-agent system environment. We corroborate our results with numerical examples, which show the performance interdependencies between two CPSs through cyber and physical interactions.

The objective of this paper is to explore the current notions of systems and “System of Systems” and establish the case for quantitative characterization of their structural, behavioural and contextual facets that will pave the way for further formal development (mathematical formulation). This is partly driven by stakeholder needs and perspectives and also in response to the necessity to attribute and communicate the properties of a system more succinctly, meaningfully and efficiently. The systematic quantitative characterization framework proposed will endeavor to extend the notion of emergence that allows the definition of appropriate metrics in the context of a number of systems ontologies. The general characteristic and information content of the ontologies relevant to system and system of system will be specified but not developed at this stage. The current supra-system, system and sub-system hierarchy is also explored for the formalisation of a standard notation in order to depict a relative scale and order and avoid the seemingly arbitrary attributions.