Encrypted Network Traffic Analysis
Springer International Publishing (Verlag)
978-3-031-62908-2 (ISBN)
This book provides a detailed study on sources of encrypted network traffic, methods and techniques for analyzing, classifying and detecting the encrypted traffic. The authors provide research findings and objectives in the first 5 chapters, on encrypted network traffic, protocols and applications of the encrypted network traffic. The authors also analyze the challenges and issues with encrypted network traffic. It systematically introduces the analysis and classification of encrypted traffic and methods in detecting the anomalies in encrypted traffic. The effects of traditional approaches of encrypted traffic, such as deep packet inspection and flow based approaches on various encrypted traffic applications for identifying attacks is discussed as well. This book presents intelligent techniques for analyzing the encrypted network traffic and includes case studies.
The first chapter also provides fundamentals of network traffic analysis, anomalies in the network traffic, protocols for encrypted network traffic. The second chapter presents an overview of the challenges and issues with encrypted network traffic and the new threat vectors introduced by the encrypted network traffic. Chapter 3 provides details analyzing the encrypted network traffic and classification of various kinds of encrypted network traffic. Chapter 4 discusses techniques for detecting attacks against encrypted protocols and chapter 5 analyzes AI based approaches for anomaly detection.
Researchers and professionals working in the related field of Encrypted Network Traffic will purchase this book as a reference. Advanced-level students majoring in computer science will also find this book to be a valuable resource.
Dr. Aswani Kumar Cherukuri is a Professor (Higher Academic Grade) of School of Computer Science Engineering & Information Systems, Vellore Institute of Technology, Vellore, India. His research interests are machine learning, information security and quantum computing. In particular, his work is focused on encrypted network traffic analysis, machine learning techniques. Also, he has interests in post quantum cryptography. He published more than 190 research papers and has 4100+ citations and h-index of 31 as per Google scholar. He executed as principal investigator, different research projects of worth 10 million USD from various funding agencies of India. He has guided 8 PhD research scholars and few foreign interns. He has received awards including Young Scientist Fellowship, Inspiring Teacher Award, Educator excellence award, etc. He is editorial board member of several international journals. He is a member of IEEE, Senior Member of ACM, Vice Chair of IEEE Educational Taskforce on Datamining.
Dr. Sumaiya Thaseen Ikram is an Associate Professor (Senior) in the School of Computer Science and Information Systems in Vellore Institute of Technology, Vellore with 18 years of teaching and research experience. She has expertise in Cryptography, Network Security, Software Security, Intrusion Detection Systems, Artificial Intelligence, Image Processing, Ethical hacking, Vulnerability Assessment and Penetration Testing. She has 1400+ citations and h-index of 15 in google scholar and most of her research works are published in high impact factor journals. She is a reviewer for many journals of Wiley, Elsevier, and Springer publishers. She is a certified ethical hacker, certified penetration testing engineer and certified computer hacking forensic investigator. She has successfully completed a research project as a Co-PI funded by MHRD worth Rs.63 lakhs between 2019-2023 in collaboration with Deakin University, Australia. She also completed a consultancy project worth Rs.6 lakhs in the domain of full stack development in the year 2022. She has delivered many expert talks in the domain of Intrusion detection Systems in Taylors University, Malaysia and Deakin University, Australia.
Dr. Gang Li is the university academic board member and full professor at Deakin University, and he is the AI director in the Strategic Research Center of Cyber Resilience and Trust (CREST). His research includes data mining, privacy preservation, group behavior analysis and business intelligence. He holds one international patent, and he has co-authored nine papers that won best paper prizes, including Springer's Journal of IT & Tourism best paper award in 2023, KSEM 2018 best paper award, IFITT Journal Paper of the Year 2018/2015, ACM/IEEE ASONAM2012 best paper award, the 2008 Nightingale Prize by Springer, etc.
Dr. Xiao Liu received his bachelor's and master's degrees in information management and information system from the School of Management, Hefei University of Technology, Hefei, China, in 2004 and 2007, respectively, and his Ph.D. degree in computer science and software engineering from the Faculty of Information and Communication Technologies, Swinburne University of Technology, Melbourne, Australia, in 2011. He was an Associate Professor at the Software Engineering Institute, East China Normal University, Shanghai, China, during 2013 to 2015. He is currently an Associate Professor and Director for the Software Engineering Innovation Lab with the School of Information Technology, Deakin University, Australia. His research interests include workflow systems, cloud and edge computing, big data analytics, social network, and human-centric software engineering. He is a Senior Member of ACM and IEEE.
Preface.- Acknowledgement.- Chapter 1 Introduction.- Chapter 2 Encrypted Network Traffic Analysis.- Chapter 3 Classification of Encrypted Network Traffic.- Chapter 4 Detection of Anomalous Encrypted Traffic.- Chapter 5 AI based Approaches for Anomaly Detection.
| Erscheinungsdatum | 26.07.2024 |
|---|---|
| Reihe/Serie | SpringerBriefs in Computer Science |
| Zusatzinfo | XII, 99 p. 27 illus., 14 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 155 x 235 mm |
| Themenwelt | Informatik ► Netzwerke ► Sicherheit / Firewall |
| Informatik ► Theorie / Studium ► Kryptologie | |
| Schlagworte | Adversary • anomalies • Deep learning • Deep Packet Inspection • Encryption • Explainable AI • Flow based Features • Infiltration • machine learning • Malware • Network traffic • Packet Filtering • Parameter tuning • Performance • protocols • raw data • security • Statistical Techniques • threat vector • traffic classification |
| ISBN-10 | 3-031-62908-6 / 3031629086 |
| ISBN-13 | 978-3-031-62908-2 / 9783031629082 |
| Zustand | Neuware |
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