Machine Intelligence and Big Data Analytics for Cybersecurity Applications

ISBN-13: 9783030570231 / Angielski / Twarda / 2020 / 539 str.

Yassine Maleh; Mohammad Shojafar; Mamoun Alazab

Spis treści

Network Intrusion Detection: Taxonomy and Machine Learning Applications.- Machine Learning and Deep Learning models for Big Data Issues.- The Fundamentals and Potential for Cybersecurity of Big Data in the Modern World.- Improving Cyber-Threat Detection by Moving the Boundary around the Normal Samples.- Bayesian Networks for Online Threat Detection.- Network Intrusion Detection for TCP/IP Packets with Machine Learning Techniques.- Developing a Blockchain-based and Distributed Database-oriented Multi-Malware Detection Engine.- Classifying Common Vulnerabilities and Exposures Database Using Text Mining and Graph Theoretical Analysis.- Robust Cryptographical Applications for a Secure Wireless Network Protocol.

Blurb

This book presents the latest advances in machine intelligence and big data analytics to improve early warning of cyber-attacks, for cybersecurity intrusion detection and monitoring, and malware analysis. Cyber-attacks have posed real and wide-ranging threats for the information society. Detecting cyber-attacks becomes a challenge, not only because of the sophistication of attacks but also because of the large scale and complex nature of today’s IT infrastructures. It discusses novel trends and achievements in machine intelligence and their role in the development of secure systems and identifies open and future research issues related to the application of machine intelligence in the cybersecurity field. Bridging an important gap between machine intelligence, big data, and cybersecurity communities, it aspires to provide a relevant reference for students, researchers, engineers, and professionals working in this area or those interested in grasping its diverse facets and exploring the latest advances on machine intelligence and big data analytics for cybersecurity applications.