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Supervisory Control and Data Acquisition (SCADA) systems face the absence of a protection technique that can beat different types of intrusions and protect the data from disclosure while handling this data using other applications, specifically Intrusion Detection System (IDS). The SCADA system can manage the critical infrastructure of industrial control environments. Protecting sensitive information is a difficult task to achieve in reality with the connection of physical and digital systems. Hence, privacy preservation techniques have become effective in order to protect sensitive/private information and to detect malicious activities, but they are not accurate in terms of error detection, sensitivity percentage of data disclosure. In this paper, we propose a new Privacy Preservation Intrusion Detection (PPID) technique based on the correlation coefficient and Expectation Maximisation (EM) clustering mechanisms for selecting important portions of data and recognizing intrusive events. This technique is evaluated on the power system datasets for multiclass attacks to measure its reliability for detecting suspicious activities. The experimental results outperform three techniques in the above terms, showing the efficiency and effectiveness of the proposed technique to be utilized for current SCADA systems.

Abstract-With the ubiquitous computing of providing services and applications at anywhere and anytime, cloud computing is the best option as it offers flexible and pay-per-use based services to its customers. Nevertheless, security and privacy are the main challenges to its success due to its dynamic and distributed architecture, resulting in generating big data that should be carefully analysed for detecting network's vulnerabilities. In this paper, we propose a Collaborative Anomaly Detection Framework (CADF) for detecting cyber attacks from cloud computing environments. We provide the technical functions and deployment of the framework to illustrate its methodology of implementation and installation. The framework is evaluated on the UNSW-NB15 dataset to check its credibility while deploying it in cloud computing environments. The experimental results showed that this framework can easily handle large-scale systems as its implementation requires only estimating statistical measures from network observations. Moreover, the evaluation performance of the framework outperforms three state-of-the-art techniques in terms of false positive rate and detection rate.

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Cyber-Physical Systems (CPSs) as in power, water, gas systems and smart grids produce heterogeneous and huge amount of data from various devices and networks, therefore it is essential to develop efficient privacy-preserving techniques for safeguarding system data (telemetry and transactions) from cyber attacks. In order to protect CPSs from prospect cyber-threats, it is essential to discover system components vulnerabilities to prohibit Advanced Persistent Threats (APTs) and keep their generated data secure using privacy-preserving techniques. 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.