Roy S., Chakraborty R.S., Mathew J., Mazumdar A.P., Chakraborty S. (eds.) Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis

CRC Press, 2023. — 137 p. — (Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series). — ISBN: 978-1-032-46138-0.Artificial Intelligence and Deep Learning for Computer Network: Management and Analysis aim to systematically collect quality research spanning AI, ML, and Deep Learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights into the applicability of the theoretical similitudes, otherwise a rarity in many such books.In recent years, particularly with the advent of Deep Learning (DL), new avenues have opened up to handle today’s most complex and very dynamic computer networks and the large amount of data (often real-time) that they generate. Artificial Intelligence (AI) and Machine Learning (ML) techniques have already shown their effectiveness in different networks and service management problems, including, but not limited to, cloud, traffic management, cybersecurity, etc. There exist numerous research articles in this domain, but a comprehensive and self-sufficient book capturing the current state-of-the-art has been lacking. The book aims to systematically collect quality research spanning AI, ML, and Deep Learning (DL) applications to diverse sub-topics of computer networks, communications, and security, under a single cover. It also aspires to provide more insights into the applicability of the theoretical similitudes, otherwise a rarity in many such books.In the first chapter, the application of Machine Learning to traffic management, in particular, the classification of domain name service (DNS) query packets over a secure (encrypted) connection is proposed. This important problem is challenging to solve because the relevant fields in the packet header and body that allows easy classification are not available in plaintext in an encrypted packet. An accurate Deep Learning model and a support vector machine (SVM) – based ML model are based on well-chosen features that were constructed to solve this problem.Features:A diverse collection of important and cutting-edge topics covered in a single volume.
Several chapters on cybersecurity, an extremely active research area.
Recent research results from leading researchers and some pointers to future advancements in methodology.
Detailed experimental results obtained from standard data sets.Deep Learning in Traffic Management: Deep Traffic Analysis of Secure DNS.
Machine Learning – Based Approach for Detecting Beacon Forgeries in Wi-Fi Networks.
Reinforcement Learning – Based Approach Towards Switch Migration for Load-Balancing in SDN.
Green Corridor over a Narrow Lane: Supporting High-Priority Message Delivery through NB-IoT.
Vulnerabilities Detection in Cybersecurity Using Deep Learning – Based Information Security and Event Management.
Detection and Localization of Double-Compressed Forged Regions in JPEG Images Using DCT Coefficients and Deep Learning – Based CNN.