Guest Editors
Dr. Sicong Shao
Email: sicong.shao@und.edu
Affiliation: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, 58202, United States
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Research Interests: cybersecurity, machine learning, and software engineering
Dr. Tingjun Lei
Email: tingjun.lei@und.edu
Affiliation: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, 58202, United States
Homepage:
Research Interests: bio-inspired artificial intelligence (AI), robotics and autonomous systems, optimization and evolutionary computation, human-autonomy teaming (HAT), intelligent transportation systems, and applied machine learning
Dr. Jielun Zhang
Email: jielun.zhang@und.edu
Affiliation: School of Electrical Engineering and Computer Science, University of North Dakota, Grand Forks, 58202, United States
Homepage:
Research Interests: Networking analytics, Artificial Intelligence, Network security, Cybersecurity, Internet-of-things
Summary
Intrusion Detection and Prevention Systems (IDS/IPS) are foundational components of modern cybersecurity. They serve as essential guards, monitoring network traffic via Network IDS/IPS and endpoint activities via Host IDS/IPS. Their goals are to detect, identify, block, and report malicious operations, policy violations, and anomalous behaviors before they can result in significant compromise.
This Special Issue aims to gather high-quality, original research that contributes to pushing beyond signature-based methods toward adaptive, explainable, and resilient defences spanning cloud, edge, IoT/IIoT, and cyber-physical systems. The focus is on advanced methods and solutions that enhance the effectiveness, efficiency, and scalability of IDS/IPS, thereby proposing novel and impactful approaches. Both original research papers and reviews are welcome. Research may focus on (but is not limited to) the following topics:
· Advanced Anomaly Detection Models for IDS/IPS
· Analysis of Encrypted and Obfuscated Traffic for IDS/IPS
· Cloud-Native Intrusion Detection and Prevention
· Large-Scale Distributed IDS/IPS Architectures
· Software-Defined Networking and Network Function Virtualization for IDS/IPS
· IDS/IPS for Cyber-Physical Systems and Operational Technology
· Lightweight and Efficient IDS/IPS for IoT/IIoT
· Advances in Host-Based IDS/IPS
· Advances in Network-Based IDS/IPS
· IDS/IPS Evasion and Countermeasures
· Privacy, Trust, and Explainability (XAI) in IDS/IPS
Keywords
Intrusion Detection; Intrusion Prevention; Host Security; Network Security; Machine Learning; Anomaly Detection; Privacy; Trust; Explainability; Cyber-Physical Systems ; IoT/IIoT
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