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. Key Features: 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: A Deep Learning-Based System for Network Cyber Threat Detection Angel Luis Perales Gomez, Lorenzo Fernandez Maimo and Felix J. Garcia Clemente Machine learning for phishing detection and mitigation Mohammad Alauthman, Ammar Almomani, Mohammed Alweshah, Waleed Alomoush and Kamal Alieyan Next Generation Adaptable Opportunistic Sensing Based Wireless Sensor Networks: A Machine Learning Perspective Jasminder Sandhu, Anil Verma and Prashant Rana A Bio-inspired Approach to Cyber Security Siyakha Mthunzi, Elhadj Benkhelifa, Tomasz Bosakowski and Salim Hariri Applications of a Model to Evaluate and Utilize Users’ Interactions in Online Social Networks Izzat Alsmadi and Muhammad Al-Abdullah A deep-dive on Machine learning for Cybersecurity use cases Vinayakumar R, Soman Kp, Prabaharan Poornachandran and Pradeep Menon Droid-Sec: A Prototype Method to Discover Malwares in Android-Based Smart Phones through System Calls B. B. Gupta, Shashank Gupta, Shubham Goel, Nihit Bhardwaj, and Jaiveer Singh Metaheuristic Algorithms Based Feature Selection Approach for Intrusion Detection Mohammed Al-Weshah, Saleh Al Khalayleh, Ammar Almomani, Mohammed Al-Refai and Riyadh Qashi A Taxonomy of Bitcoin Security Issues and Defense Mechanisms Prachi Gulihar and B. B. Gupta Early Detection and Prediction of Lung Cancer using Machine Learning Algorithms Applied on a Secure Healthcare Data System Architecture Mohamed Alloghani, Thar Baker, Dhiya Al-Jumeily, Abir Hussain, Ahmed Kaky and Jamila Mustafina Preventing Black Hole Attack in AODV Routing Protocol using Dynamic Trust Handshake Based Malicious Behavior Detection Mechanism Bhawna Singla, A.K. Verma and L.R. Raheja Detecting Controller Interlock based Tax Evasion Groups in a Corporate Governance Network Jianfei Ruan, Zheng Yan, Bo Dong and Qinghua Zheng Defending Web Applications against JavaScript Worms on Core Network of Cloud Platforms Shashank Tripathi, Pranav Saxena, Harsh Dwivedi and Shashank Gupta Importance of Providing Incentives and Economic Solutions in IT Security A. Dhahiya, and B. B. Gupta Teaching Johnny to Thwart Phishing Attacks: Incorporating the Role of Self-Efficacy into a Game Application Nalin Asanka Gamagedara Arachchilage, and Mumtaz Abdul Hameed

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/