Machine Learning for Computer and Cyber Security
Principle, Algorithms, and Practices
While Computer Security is a broader term which incorporates technologies, protocols, standards and policies to ensure the security of the computing systems including the computer hardware, software and the information stored in it, Cyber Security is a specific, growing field to protect computer networks (offline and online) from unauthorized access, botnets, phishing scams, etc. Machine learning is a branch of Computer Science which enables computing machines to adopt new behaviors on the basis of observable and verifiable data and information. It can be applied to ensure the security of the computers and the information by detecting anomalies using data mining and other such techniques.
This book will be an invaluable resource to understand the importance of machine learning and data mining in establishing computer and cyber security. It emphasizes important security aspects associated with computer and cyber security along with the analysis of machine learning and data mining based solutions. The book also highlights the future research domains in which these solutions can be applied. Furthermore, it caters to the needs of IT professionals, researchers, faculty members, scientists, graduate students, research scholars and software developers who seek to carry out research and develop combating solutions in the area of cyber security using machine learning based approaches. It is an extensive source of information for the readers belonging to the field of Computer Science and Engineering, and Cyber Security professionals.
- This book contains examples and illustrations to demonstrate the principles, algorithms, challenges and applications of machine learning and data mining for computer and cyber security.
- It showcases important security aspects and current trends in the field.
- It provides an insight of the future research directions in the field.
- Contents of this book help to prepare the students for exercising better defense in terms of understanding the motivation of the attackers and how to deal with and mitigate the situation using machine learning based approaches in better manner.
Table of Contents
Introduction. Classical Machine-Learning Paradigms for Data Mining. Supervised Learning for Misuse/Signature Detection. Machine Learning for Anomaly Detection. Machine Learning for Hybrid Detection. Machine Learning for Scan Detection. Machine Learning for Profiling Network Traffic. Privacy-Preserving Data Mining. Emerging Challenges in Cybersecurity.
Brij B. Gupta received PhD degree from Indian Institute of Technology Roorkee, India in Information and Cyber Security. He published more than 175 research papers in International Journals and Conferences of high repute including IEEE, Elsevier, ACM, Springer, Wiley, Taylor & Francis, Inderscience, etc. He has visited several countries, i.e. Canada, Japan, Malaysia, Australia, China, Hong-Kong, Italy, Spain etc to present his research work. His biography was selected and published in the 30th Edition of Marquis Who's Who in the World, 2012. Dr. Gupta also received Young Faculty research fellowship award from Ministry of Electronics and Information Technology, Government of India in 2017. He is also working as principal investigator of various R&D projects. He is serving as associate editor of IEEE Access, IEEE TII, and Executive editor of IJITCA, Inderscience, respectively. At present, Dr. Gupta is working as Assistant Professor in the Department of Computer Engineering, National Institute of Technology Kurukshetra India. His research interest includes Information security, Cyber Security, Mobile security, Cloud Computing, Web security, Intrusion detection and Phishing.
Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University. Before moving to Macquarie, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Sheng also worked at UNSW as a visiting research fellow. Prior to that, he spent 6 years as a senior software engineer in industries.
Prof. Sheng has more than 280 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences including ACM Computing Surveys, ACM TOIT, ACM TOMM, ACM TKDD, VLDB Journal, Computer (Oxford), IEEE TPDS, TKDE, DAPD, IEEE TSC, WWWJ, IEEE Computer, IEEE Internet Computing, Communications of the ACM, VLDB, ICDE, ICDM, CIKM, EDBT, WWW, ICSE, ICSOC, ICWS, and CAiSE. Dr. Michael Sheng is the recipient of the ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). He is a member of the IEEE and the ACM. Homepage: https://web.science.mq.edu.au/~qsheng/