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Ms. Pooja Dwivedi is an Assistant Professor in the Department of Computer Applications at Axis Colleges, Kanpur, India. Her research interests include Information Security, Machine Learning, Cybersecurity Analytics, and Intelligent Security Systems.
The escalating complexity of cyberattacks and the swift proliferation of interconnected networks have posed substantial hurdles for conventional Intrusion Detection Systems (IDS). Deep Learning (DL) has arisen as an effective method for identifying and categorising cyber risks by autonomously discerning intricate patterns from extensive network traffic and security data. Deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Autoencoders, and Transformer architectures, have exhibited exceptional proficiency in detecting both known and new assaults with high precision. This chapter examines the principles, designs, techniques, and applications of intrusion detection systems based on deep learning. The text addresses data pretreatment approaches, feature engineering, model construction, performance evaluation, real-world implementations, obstacles, and future research directions, highlighting the significance of deep learning in enhancing contemporary cybersecurity frameworks.