Protecting Cyber-physical Systems (CPSs) is highly important for preserving sensitive information and detecting cyber threats. Developing a robust privacy-preserving anomaly detection method requires physical and network data about the systems, such as Supervisory Control and Data Acquisition (SCADA), for protecting original data and recognising cyber-attacks. In this paper, a new privacy-preserving anomaly detection framework, so-called PPAD-CPS, is proposed for protecting confidential information and discovering malicious observations in power systems and their network traffic. The framework involves two main modules. Firstly, a data pre-processing module is suggested for filtering and transforming original data into a new format that achieves the target of privacy preservation. Secondly, an anomaly detection module is suggested using a Gaussian Mixture Model (GMM) and Kalman Filter (KF) for precisely estimating the posterior probabilities of legitimate and anomalous events. The performance of the PPAD-CPS framework is assessed using two public datasets, namely the Power System and UNSW-NB15 dataset. The experimental results show that the framework is more effective than four recent techniques for obtaining high privacy levels. Moreover, the framework outperforms seven peer anomaly detection techniques in terms of detection rate, false positive rate and computational time.

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.

The performance of Artificial Intelligence (AI) systems reaches or even exceeds that of humans in an increasing number of complicated tasks. Highly effective non-linear AI models are generally employed in a black-box form nested in their complex structures, which means that no information as to what precisely helps them reach appropriate predictions is provided. The lack of transparency and interpretability in existing Artificial Intelligence techniques would reduce human users’ trust in the models used for cyber defence, especially in current scenarios where cyber resilience is becoming increasingly diverse and challenging. Explainable AI (XAI) should be incorporated into developing cybersecurity models to deliver explainable models with high accuracy that human users can understand, trust, and manage. This paper explores the following concepts related to XAI. A summary of current literature on XAI is discussed. Recent taxonomies that help explain different machine learning algorithms are discussed. These include deep learning techniques developed and studied extensively in other IoT taxonomies. The outputs of AI models are crucial for cybersecurity, as experts require more than simple binary outputs for examination to enable the cyber resilience of IoT systems. Examining the available XAI applications and safety-related threat models to explain resilience towards IoT systems also summarises the difficulties and gaps in XAI concerning cybersecurity. Finally, various technical issues and trends are explained, and future studies on technology, applications, security, and privacy are presented, emphasizing the ideas of explainable AI models.