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Industrial Control Systems (ICSs) have entered an era of modernization enabled by the recent progress in Information Technologies (IT), particularly the Industrial Internet of Things (IIoT). This enables better automation of industrial processes but now exposes the ICSs to cyber-attacks that exploit the IIoT vulnerabilities. Thus, to ensure ICSs security, numerous research works have focused on designing Intrusion Detection and Prevention Systems (IDPSs), and deep learning has recently received considerable attention, as it has the potential to improve detection accuracy. However, most of the proposed deep learning solutions focus only on the model’s accuracy without considering latency, which is an essential requirement in many ICSs. The novelty of this paper is the time complexity analysis of Deep Neural Networks (DNNs) and the design of a low latency and robust deep learning-based collaborative IDPS. The proposed architecture employs two classification models. In the first model, a lightweight DNN is used to perform a binary classification, i.e., normal or attack, which ensures rapid intrusion detection. A second model ensures the identification of the type of attacks by performing a multi-class classification of the detected anomaly, which is handled by a robust and complex DNN in order to achieve higher accuracy. This research also proposes intrusion response measures to deal with detected attacks, first after the anomaly detection, and then after the identification of the attack type. An experimental evaluation has been provided using various detection features, datasets, DNN algorithms, and the results demonstrate the effectiveness of the proposed solution.

Currently, the Intrusion Detection System (IDS) is attracting both the commercial companies and the research community as IDS is playing an increasingly important role in most network systems to detect and block possi-ble attacks. This paper aims to present a general view of the state-of-the-art of the IDS, based on a proposed taxonomy so that the researchers can quickly become familiar with the essential aspects of the intrusion detection techniques. The taxonomy includes reclassifying IDSs according to multiple bases, e.g., IDS' data source, the detection method, and others. Also, comparisons among different detection approaches and various data collection techniques are tabulated. Besides, the paper exhibits the taxonomy of anomaly-based IDSs classifying the promising techniques and summarizing the merits of the most recent anomaly-based techniques as a table. Furthermore, it deliberates the IDSs through various applications, like data centers, backbone, Fog, and Cloud Computing, and IoT models, in addition to the most common prevailing models. Subsequently, multiple metrics and the datasets frequently used to assess the IDS are described concisely. Finally, the requirements and challenges of contemporary IDSs are mapped. [ABSTRACT FROM AUTHOR]

Cyber threats are serious concerns for power systems. For example, hackers may attack power control systems via interconnected enterprise networks. This paper proposes a risk assessment framework to enhance the resilience of power systems against cyber attacks. The duality element relative fuzzy evaluation method is employed to evaluate identified security vulnerabilities within cyber systems of power systems quantitatively. The attack graph is used to identify possible intrusion scenarios that exploit multiple vulnerabilities. An intrusion response system (IRS) is developed to monitor the impact of intrusion scenarios on power system dynamics in real time. IRS calculates the conditional Lyapunov exponents (CLEs) on line based on the phasor measurement unit data. Power system stability is predicted through the values of CLEs. Control actions based on CLEs will be suggested if power system instability is likely to happen. A generic wind farm control system is used for case study. The effectiveness of IRS is illustrated with the IEEE 39 bus system model.