Guest Editors
Dr. Ateeq Ur Rehman
Email: 202411144@gachon.ac.kr
Affiliation: School of Computing, Gachon University, Seongnam-Si, Republic of Korea
Homepage:
Research Interests: artificial intelligence, cybersecurity, big data
Prof. Habib Hamam
Email: habib.hamam@umoncton.ca
Affiliation: Faculty of Engineering, Uni de Moncton, Moncton, NB, Canada
Homepage:
Research Interests: artificial intelligence-based design
Dr. Salil Bharany
Email: salil.bharany@gmail.com
Affiliation: Department of Computer Engineering & Technology, Guru Nanak Dev University, Amritsar, India
Homepage:
Research Interests: cybersecurity, AI-bioinspired
Mr. Tehseen Mazhar
Email: tehseenmazhar719@gmail.com
Affiliation: School of Computer Science, National College of Business Administration and Economics, Lahore, Pakistan
Homepage:
Research Interests: machine mearning, cloud computing, blockchain
Summary
For years, designing intrusion detection systems (IDS) that can handle rising traffic and new cyberattacks has been a challenge. Though there has been significant advancement in this area, however, there is still a need of modern, robust and advanced machine learning techniques to detect previously unknown threads with higher accuracy.
An efficient intrusion detection system is essential since technological advancements embark on new kinds of attacks and security limitations. Traditional IDS models may perform poorly with modern datasets that better reflect network traffic patterns. Class imbalance, attack type representation, and precise traffic classification make creating realistic datasets difficult. Recent advances in network intrusion detection have been made by integrating machine learning (ML) and artificial intelligence (AI) models. Advanced AI/ML models can automatically identify traffic features connected to intrusions at multiple abstraction layers, even in massive data volumes.
The scope of this special issue is to improve AI/ML intrusion detection models' complexity, applicability, flexibility, and explainability. We also accept papers that propose novel algorithms, techniques, or methodologies to enhance the detection of new and evolving intrusions. Additionally, we encourage submissions that provide high-quality datasets to address the challenges of class imbalance, attack type representation, and precise traffic classification.
We invite high-quality, original research papers and review articles addressing, but not limited to, the following topics:
· AI-Enhanced Network Intrusion Detection Techniques
· Explainable Machine Learning Models for Cybersecurity
· Hybrid and Ensemble Methods for Threat Detection
· Intrusion Detection in IoT and Cloud Environments
· Deep Learning Architectures for Anomaly and Signature-Based Detection
· Creation and Use of Benchmark Datasets for IDS
· Real-Time and Scalable Intrusion Detection Systems
Keywords
intrusion detection system, machine learning, deep learning, cybersecurity, advances in artificial intelligence.
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