About the Book
In recent years, a considerable amount of effort has been devoted to cyber-threat protection of computer systems which is one of the most critical cybersecurity tasks for single users and businesses since even a single attack can result in compromised data and sufficient losses. Massive losses and frequent attacks dictate the need for accurate and timely detection methods. Current static and dynamic methods do not provide efficient detection, especially when dealing with zero-day attacks. For this reason, big data analytics and machine intelligencebased techniques can be used.
This book brings together researchers in the field of big data analytics and intelligent systems for cyber threat intelligence CTI and key data to advance the mission of anticipating, prohibiting, preventing, preparing, and responding to internal security. The wide variety of topics it presents offers readers multiple perspectives on various disciplines related to big data analytics and intelligent systems for cyber threat intelligence applications.
Technical topics discussed in the book include:
• Big data analytics for cyber threat intelligence and detection
• Artificial intelligence analytics techniques
• Real-time situational awareness
• Machine learning techniques for CTI
• Deep learning techniques for CTI
• Malware detection and prevention techniques
• Intrusion and cybersecurity threat detection and analysis
• Blockchain and machine learning techniques for CTI
Table of Contents:
1 Cyber Threat Intelligence Model: An Evaluation of Taxonomies and Sharing Platforms 2 Evaluation of Open-sourceWeb Application Firewalls for Cyber Threat Intelligence 3 Comprehensive Survey of Location Privacy and Proposed Effective Approach to Protecting the Privacy of LBS Users 4 Analysis of Encrypted Network Traffic using Machine Learning Models 5 Comparative Analysis of Android Application Dissection and Analysis Tools for Identifying Malware Attributes 6 Classifying Android PendingIntent Security using Machine Learning Algorithms 7 Machine Learning and Blockchain Integration for Security Applications 8 Cyberthreat Real-time Detection Based on an Intelligent Hybrid Network Intrusion Detection System 9 Intelligent Malware Detection and Classification using Boosted Tree Learning Paradigm 10 Malware and Ransomware Classification, Detection, and Prevention using Artificial Intelligence (AI) Techniques 11 Detecting High-quality GAN-generated Face Images using Neural Networks 12 Fault Tolerance of Network Routers using Machine Learning Techniques
About the Author :
Yassine Maleh is currently an Associate Professor of cybersecurity and IT governance with Sultan Moulay Slimane University, Morocco. He is the founding chair of IEEE Consultant Network Morocco and the founding president of the African Research Center of Information Technology & Cybersecurity. He is a senior member of IEEE and a member of the International Association of Engineers IAENG and The Machine Intelligence Research Labs. Dr. Maleh has made contributions in the fields of information security and privacy, Internet of Things security, and wireless and constrained networks security. His research interests include information security and privacy, Internet of Things, networks security, information system, and IT governance. He has published over 100 papers (book chapters, international journals, and conferences/workshops), 20 edited books, and 3 authored books. He is the editor-in-chief for the International Journal of Information Security and Privacy and the International Journal of Smart Security Technologies (IJSST). He serves as an associate editor for IEEE Access (2019 Impact Factor 4.098), the International Journal of Digital Crime and Forensics (IJDCF), and the International Journal of Information Security and Privacy (IJISP). He is a series editor of Advances in Cybersecurity Management, by CRC Taylor & Francis. He was also a guest editor of a special issue on Recent Advances on Cyber Security and Privacy for Cloud-of-Things of the International Journal of Digital Crime and Forensics (IJDCF), Volume 10, Issue 3, July-September 2019. He has served and continues to serve on executive and technical program committees and as a reviewer of numerous international conferences and journals such as Elsevier Ad Hoc Networks, IEEE Network Magazine, IEEE Sensor Journal, ICT Express, and Springer Cluster Computing. He was the publicity chair of BCCA 2019 and the general chair of the MLBDACP 19 Symposium and ICI2C'21 Conference. He received Publon Top 1% reviewer award for the years 2018 and 2019.
Dr. Mamoun Alazab is currently an Associate Professor with the College of Engineering, IT and Environment, Charles Darwin University, Australia. He received the Ph.D. degree in computer science from the Federation University of Australia, School of Science, Information Technology and Engineering. He is a cybersecurity researcher and practitioner with industry and academic experience. Dr Alazab's research is multidisciplinary that focuses on cybersecurity and digital forensics of computer systems including current and emerging issues in the cyber environment like cyber-physical systems and the Internet of Things, by taking into consideration the unique challenges present in these environments, with a focus on cybercrime detection and prevention. He looks into the intersection use of machine learning as an essential tool for cybersecurity, for example, for detecting attacks, analyzing malicious code, or uncovering vulnerabilities in software. He has more than 100 research papers. He is the recipient of short fellowship from Japan Society for the Promotion of Science (JSPS) based on his nomination from the Australian Academy of Science. He delivered many invited and keynote speeches, 27 events in 2019 alone. He convened and chaired more than 50 conferences and workshops. He is the founding chair of the IEEE Northern Territory Subsection (February 2019 to present). He is a senior member of the IEEE, Cybersecurity Academic Ambassador for Oman's Information Technology Authority (ITA), member of the IEEE Computer Society's Technical Committee on Security and Privacy (TCSP), and has worked closely with government and industry on many projects, including IBM, Trend Micro, the Australian Federal Police (AFP), the Australian Communications and Authority (ACMA), Westpac, UNODC, and the Attorney General's Department.
Dr. Lo'ai Tawalbeh received the Ph.D. degree in electrical & computer engineering from Oregon State University in 2004, and the M.Sc. in 2002 from the same university with GPA 4/4. Dr. Tawalbeh is currently an Associate Professor with the Department of Computing and Cyber Security, Texas A&M University-San Antonio. Before that, he was a Visiting Researcher with the University of California-Santa Barbra. Since 2005, he has been teaching/developing more than 25 courses in different computer engineering disciplines and science with a focus on cybersecurity for the undergraduate/graduate programs at the NewYork Institute of Technology (NYIT), DePaul's University, and Jordan University of Science and Technology. Dr. Tawalbeh won many research grants and awards with over 2 Million USD. He has over 80 research publications in refereed international journals and conferences.
Imed Romdhani has been an Associate Professor in networking with Edinburgh Napier University since June 2005. He received the Ph.D. degree from the University of Technology of Compiegne (UTC), France, in May 2005. He also holds an engineering and a Master's degree in networking obtained, respectively, in 1998 and 2001 from the National School of Computing (ENSI, Tunisia) and Louis Pasteur University of Strasbourg (ULP, France). He worked extensively with Motorola Research Labs in Paris and authored four patents.
