Commerce, healthcare, manufacturing, and energy are just some of the sectors of modern society that have been revolutionized by the adoption of computer systems and the penetration of digital communications. With this digitization trend expanding with increasing rates, cyber-attacks and threats have also become an omnipresent, all-pervasive phenomenon. It is because of this penetration that today more than ever, attackers have high motivation to perform well-orchestrated attacks. To make matters worse, attackers can rely on publicly available offensive tools or acquire exploits from the dark web. It does not come as a surprise that attacks and malware become increasingly intelligent, stealthy, and robust against traditional defense practices. Recent incidents like Stuxnet or Wannacry signify the urgency for developing intelligent detection methodologies and tools able to identify never-seen-before threats. Artificial Intelligence (AI), Machine Learning (ML), and data analysis methods, while applied successfully to other domains, have only seen partial practical application in intrusion detection. The primary reasons that have been identified in the literature are: (a) high false-positive rates, (b) lack of rich data to train effective models due to the sensitive nature of the security domain, (c) requirement for an elaborate feature engineering phase conducted by human domain-experts, and (d) the inability of existing methods to create explainable models. The objective of this Special Issue is to provide the state-of-the-art in the field of anomaly and intrusion detection giving particular emphasis to intelligent techniques that are able to overcome one or all of the well-documented inefficiencies of the existing approaches. Researchers are invited to contribute novel methods, algorithms, datasets, tools, and studies in the field.

Keywords

- Scallable Anomaly Detection Methods
- Distributed Intrusion Detection
- Collaborative Intrusion Detection
- Privacy Preserving IDS
- Federated Anomaly Detection
- Application of Deep Learning for Intrusion Detection
- Reinforcement Learning for Intrusion Detection
- Intrusion Detection in IoT Networks
- Intrusion Detection for Industrial Control Systems
- Intrusion Detection in Vehicular Networks

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Guest Editors
Dr. Georgios Kambourakis
Dr. Weizhi Meng
Dr. Konstantinos Kolias