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Suraj Yadav is an Assistant Professor in the Faculty of Engineering and Technology at Jagannath University, Jaipur, Rajasthan, India. His academic role is associated with engineering and technology education. His contribution addresses deep learning applications for cybersecurity and intelligent intrusion detection.
The swift expansion of digital infrastructure, cloud computing, the Internet of Things (IoT), and interconnected communication systems has markedly heightened the intricacy of cybersecurity threats. Conventional security measures, such as signature-based intrusion detection systems, frequently fail to recognise novel attacks, advanced malware, and changing network incursion trends. Deep learning presents novel prospects for cybersecurity by autonomously acquiring intricate representations from network traffic, system logs, authentication records, and various security-related information. The section additionally addresses benchmark datasets, assessment metrics, adversarial resilience, interpretable artificial intelligence, privacy safeguards, and deployment factors. Special emphasis is placed on class imbalance, minimising false positives, real-time processing, idea drift, and the ability to generalise to novel threats. The chapter wraps off by outlining prospective research avenues, such as federated learning, self-supervised learning, multimodal security analytics, and collaborative threat intelligence.