Artificial Intelligence and Swarm Intelligence for Intrusion Detection Systems
Artificial Intelligence and Swarm Intelligence for Intrusion Detection Systems examines the application of intelligent computational techniques to the design and development of intrusion detection systems for modern computer and network environments. The book focuses on how artificial intelligence can support security monitoring, anomaly identification, attack classification, and adaptive analysis of network activity. It also introduces swarm intelligence as a complementary computational paradigm for solving complex optimization and search problems associated with cybersecurity. Key concepts include intelligent intrusion detection, network security, anomaly detection, machine learning, computational intelligence, optimization, threat identification, feature analysis, and security data processing. The subject is particularly relevant to the growing need for automated approaches capable of analyzing large and changing volumes of network traffic and identifying patterns associated with potentially malicious behavior. By connecting artificial intelligence with swarm-based optimization and collective problem-solving principles, the book provides a technical perspective on approaches that can be considered when developing intelligent security mechanisms. It is intended to support readers seeking a focused understanding of AI-driven intrusion detection concepts, algorithmic approaches, and the role of optimization in cybersecurity. The material can be useful to researchers, postgraduate students, computer science professionals, cybersecurity practitioners, and readers interested in artificial intelligence applications for network protection. The book also provides a foundation for understanding how intelligent computational methods can contribute to the broader field of cyber defense and automated security analysis
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