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Dr. R. K. Padmashini is an Associate Professor in the Department of Electrical and Electronics Engineering at AMET Deemed to be University, Chennai, Tamil Nadu. Her contribution focuses on machine learning applications in cybersecurity and intrusion detection.
The swift proliferation of digital networks, cloud computing, the Internet of Things (IoT), mobile platforms, and interconnected infrastructure has markedly heightened the intricacy of cybersecurity threats. Conventional security measures reliant on established rules and signatures may encounter challenges when addressing novel threats, extensive network traffic, and swiftly changing threat dynamics. Machine Learning (ML) offers data-centric methodologies for recognising dubious activity, recognising irregularities, categorising harmful conduct, and enhancing intelligent intrusion detection systems. This section elucidates the essential principles, techniques, and utilisations of Machine Learning within the realms of cybersecurity and intrusion detection. It examines cybersecurity threats, intrusion detection frameworks, data acquisition, feature extraction, supervised and unsupervised learning methodologies, deep learning, anomaly identification, and hybrid intelligent security systems.The section additionally addresses issues pertaining to data imbalance, adversarial assaults, privacy concerns, model interpretability, computational intricacy, and idea drift. Ultimately, novel research trajectories encompassing federated learning, interpretable AI, graph-oriented security analytics, edge computing AI, and self-sufficient cyber defence are introduced.