Biblio

Cyber-Physical Systems (CPS) underpin global critical infrastructure, including power, water, gas systems and smart grids. CPS, as a technology platform, is unique as a target for Advanced Persistent Threats (APTs), given the potentially high impact of a successful breach. Additionally, CPSs are targets as they produce significant amounts of heterogeneous data from the multitude of devices and networks included in their architecture. It is, therefore, essential to develop efficient privacy-preserving techniques for safeguarding system data from cyber attacks. This paper introduces a comprehensive review of the current privacy-preserving techniques for protecting CPS systems and their data from cyber attacks. Concepts of Privacy preservation and CPSs are discussed, demonstrating CPSs' components and the way these systems could be exploited by either cyber and physical hacking scenarios. Then, classification of privacy preservation according to the way they would be protected, including perturbation, authentication, machine learning (ML), cryptography and blockchain, are explained to illustrate how they would be employed for data privacy preservation. Finally, we show existing challenges, solutions and future research directions of privacy preservation in CPSs.

Modern power systems depend on cyber-physical systems to link physical devices and control technologies. A major concern in the implementation of smart power networks is to minimize the risk of data privacy violation (e.g., by adversaries using data poisoning and inference attacks). In this article, we propose a privacy-preserving framework to achieve both privacy and security in smart power networks. The framework includes two main modules: a two-level privacy module and an anomaly detection module. In the two-level privacy module, an enhanced-proof-of-work-technique-based blockchain is designed to verify data integrity and mitigate data poisoning attacks, and a variational autoencoder is simultaneously applied for transforming data into an encoded format for preventing inference attacks. In the anomaly detection module, a long short-term memory deep learning technique is used for training and validating the outputs of the two-level privacy module using two public datasets. The results highlight that the proposed framework can efficiently protect data of smart power networks and discover abnormal behaviors, in comparison to several state-of-the-art techniques.

Military supply chains play a critical role in the acquisition and movement of goods for defence purposes. The disruption of these supply chain processes can have potentially devastating affects to the operational capability of military forces. The introduction and integration of new technologies into defence supply chains can serve to increase their effectiveness. However, the benefits posed by these technologies may be outweighed by significant consequences to the cyber security of the entire defence supply chain. Supply chains are complex Systems of Systems, and the introduction of an insecure technology into such a complex ecosystem may induce cascading system-wide failure, and have catastrophic consequences to military mission assurance. Subsequently, there is a need for an evaluative process to determine the extent to which a new technology will affect the cyber security of military supply chains. This work proposes a new model, the Military Supply Chain Cyber Implications Model (M-SCCIM), that serves to aid military decision makers in understanding the potential cyber security impact of introducing new technologies to supply chains. M-SCCIM is a multiphase model that enables understanding of cyber security and supply chain implications through the lenses of theoretical examinations, pilot applications and system wide implementations.

Cyberspace is ubiquitous and is becoming increasingly critical to many societal, commercial, military, and national functions as it emerges as an operational space in its own right. Within this context, decision makers must achieve mission continuity when operating in cyberspace. One aspect of any comprehensive security program is the use of penetration testing; the use of scanning, enumeration and offensive techniques not unlike those used by a potential adversary. Effective penetration testing provides security insight into the network as a system in its entirety. Often though, this systemic view is lost in reporting outcomes, instead becoming a list of vulnerable or exploitable systems that are individually evaluated for remediation priority. This paper introduces Trogdor; a mission-centric automated cyber red-teaming system. Trogdor undertakes model based Automated Cyber Red Teaming (ACRT) and critical node analysis to visually present the impact of vulnerable resources to cyber dependent missions. Specifically, this work discusses the purpose of Trogdor, outlines its architecture, design choices and the technologies it employs. This paper describes an application of Trogdor to an enterprise network scenario; specifically, how Trogdor provides an understanding of potential mission impacts arising from cyber vulnerabilities and mission or business-centric decision support in selecting possible strategies to mitigate those impacts.