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K Senthil Kumar is an Assistant Professor in the Department of Networking and Communications at SRM Institute of Science and Technology, India. His contribution focuses on network security, AI, machine learning, and intelligent threat detection.
The swift advancement of digital networks, cloud computing, the Internet of Things (IoT), mobile technologies, and distributed applications has markedly heightened the intricacy of cybersecurity. Contemporary networks produce vast amounts of diverse traffic and encounter ever intricate dangers, including as malware, ransomware, phishing, distributed denial-of-service (DDoS) assaults, botnets, insider threats, zero-day vulnerabilities, and advanced persistent threats Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) offer robust functionalities for scrutinising network activity, detecting irregularities, categorising harmful actions, and forecasting possible threats. This section outlines the principal cybersecurity obstacles in contemporary networks and explores AI-based methodologies for astute threat identification. It examines network security frameworks, intrusion detection methodologies, machine-learning approaches, deep-learning architectures, anomaly identification, behavioural assessment, interpretable AI, federated learning, adversarial machine learning, and instantaneous threat intelligence. Investigations are conducted into applications within IoT, cloud computing, enterprise systems, industrial sectors, financial domains, and 5G/6G networks. The chapter wraps up by outlining new research avenues for creating adaptive, autonomous, explainable, and resilient cybersecurity systems.