Machine Learning and Deep Learning Meet Computer Networks
Description
This book presents a comprehensive exploration of how artificial intelligence techniques are transforming modern networking systems. It begins with foundational concepts in computer networks, explaining core components such as protocols, transmission media, and network architectures. The introductory chapters bridge traditional networking with machine learning (ML), highlighting how supervised, unsupervised, and reinforcement learning approaches, address challenges. These challenges range from traffic classification, quality-of-service prediction, anomaly detection to dynamic routing. A detailed treatment of deep learning (DL) architectures including CNNs, RNNs, GNNs, autoencoders, GANs, and transformers, demonstrates how complex, high-dimensional network data can be modeled effectively for optimization and security.
This book also book introduces lightweight and visual traffic-classification frameworks based on Kolmogorov-Arnold Networks (KAN), including the KAN-Vis model and the RISK-4-Auto architecture for automotive networks. It further presents hybrid deep learning approaches, such as ODENet-LSTM models for botnet detection and an optimized multi-layer intrusion detection system enhanced with genetic algorithms. Each methodology is supported by systematic experimentation and performance evaluation.
The concluding chapter outlines future directions in AI-native networking, edge intelligence, federated learning, and self-healing security architectures. This book targets researchers and professional working in this related field as well as graduate students focused on intelligent networking.
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Persons
Dr. Sangita Roy is currently an Assistant Professor in the Department of Computer Science and Engineering at Thapar Institute of Engineering & Technology (TIET), Patiala, India. She received her Ph.D. in Computer Science and Engineering with specialization in Cybersecurity and Emerging Technologies. She has authored and co-authored numerous research publications in reputed international journals and conferences, along with review papers and book chapters in emerging technology domains. She also holds 2 granted patents in the areas of intelligent IoT systems and AI-driven smart monitoring solutions, reflecting her focus on prototype-oriented and translational research. Dr. Roy is also the Founding Director of BanavatNest Private Limited, a research-driven innovation ecosystem focused on translating academic research into scalable technological solutions. Her research interests include Cybersecurity for Critical Infrastructure, AI-enabled IoT Systems, Blockchain Security, Hardware Security, and Secure Smart Manufacturing.
Pranesh Santikellur is currently an Assistant Professor at the Computer Science and Engineering Department of IIIT Bangalore, India. He holds a Doctorate in Computer Science and Engineering from the Indian Institute of Technology (IIT) Kharagpur, India. His PhD thesis focused on the design and analysis of the modeling attacks on physically unclonable functions. Following his PhD and before joining IIIT-Bangalore, he worked as a Sr. Embedded Security Researcher for nearly four years at the Secure Systems Research Center, Technology Innovation Institute, Abu Dhabi, UAE. Prior to his PhD, he worked as a firmware engineer for nearly six years in Bangalore, India. His research interests are in hardware security, embedded system security, and ML systems.
Rajat Subhra Chakraborty is currently a Professor at the Computer Science and Engineering Department of IIT Kharagpur, India. He received his Ph.D. from Case Western Reserve University (U.S.A.) and B.E. from Jadavpur University. He has professional experience of working at Intel (Bangalore, India), National Semiconductor Bangalore, India) and Advanced Micro Devices (AMD) (Santa Clara, USA). His research interests include Hardware Security, VLSI Design and Design Automation, Digital Content Protection and Digital Image Forensics. He holds 2 Granted U.S. patents, 3 granted Indian patents, and has co-authored 6 books, 10 book chapters, and over 150 publications in international journals and conferences. His work has received over 8500 citations till date, and has won 2 Best Paper awards. He has received several prestigious national and international awards such as IIT Kharagpur Outstanding Faculty Award (2018), IEI Young Engineers Award (2016), IBM Shared University Research (SUR) Award (2015), Royal Academy of Engineering (U.K.) RECI Fellowship (2014) and IBM Faculty Award (2012). He is currently an Associate Editor of the IEEE TCAD journal, and previously was an Associate Editor of the IEEE TMSCS journal. Prof. Chakraborty is a Senior Member of IEEE and a Senior Member of ACM. He currently holds the position of Vice-chair of the IEEE Kharagpur Section (R10).
Content
1 Introduction to Machine Learning for Computer Networks.- 2 An Efficient and Lightweight Visual Technique for Network Traffic Classification using Kolmogorov-Arnold Network.- 3 RISK-4-Auto: Residually Interconnected and Superimposed Kolmogorov-Arnold Networks for Automotive Network Traffic Classification.- 4 Low Computational Overhead Botnet Attack Traffic Detection using Deep Learning.- 5 Optimized Multi-Layer Hierarchical Network Intrusion Detection System with Genetic Algorithms.- 6 Looking Forward.