Guest Editors
Dr. Theyazn H.H Aldhyani, King Faisal University, King Saudi Arabia.
Dr. M. Irfan Uddin, Kohat University of Science and Technology, Pakistan.
Dr. Mohammed Y. Alzahran, Abaha university, Kingdom of Saudi Arabia.
Summary
Considering the fact that e-commerce, banking and business related highly confidential and valuable information communicated within the network, it is needless to mention the importance of network traffic analysis to attain proper information security. Network traffic analysis and prediction resembles a proactive approach rather than reactive, where network is monitored to ensure that security breaches do not occur within the network. The network traffic analysis is a significant stage for developing successful preventive congestion control schemes and to find out normal and abnormal packets from network traffic. It does not come as a surprise that attacks and malware become increasingly intelligent, stealthy, and robust against traditional defense practices. Recent incidents like Bashlite and Mirai 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. Therefore, The significance of this important topic to improve the existing and find proper ways of solving research problem in network traffic prediction and analysis intelligently. The objective of this Special Issue is to provide the state-of-the-art in the field of 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. This Special Issue provides a platform for discussing state-of-the-art in the field of 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.
Keywords
• Cybersecurity
• Cybercrime
• Security, trust, and privacy
• Anomaly intrusion detection artificial intelligence approaches
• Distributed intrusion detection artificial intelligence approaches
• Hybrid intrusion detection
• Blockchain
• Cloud computing
• Machine learning
• Deep learning
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