artificial Immune System: Applications in Computer Security focuses on the technologies and applications of AIS in malware detection proposed in recent years by the Computational Intelligence Laboratory of Peking University (CIL@PKU). It offers a theoretical perspective as well as practical solutions for readers interested in AIS, machine learning, pattern recognition, and computer security.
The book begins by introducing the basic concepts, typical algorithms, important features, and some applications of AIS. The second chapter introduces malware and its detection methods, especially for immune-based malware detection approaches. Successive chapters present a variety of advanced detection approaches for malware, including Virus Detection System, K-Nearest Neighbour (KNN), RBF networks, and Support Vector Machines (SVM), Danger Theory, Negative Selection Algorithms (NSA), Immune concentration, and immune cooperative mechanism-based learning (ICL) framework. The book concludes by presenting a new statistic named Class-Wise Information Gain (CIG), which can select features with the highest information content for a specific class in a problem, as well as efficiently detect malware loaders and infected executables in the wild.
Important features of this book:
Presents established and developed immune models for malware detection
Includes state-of-the-art malware detection techniques
Covers all of the current achievements in computer security based on immune principles, which were obtained by CIL@PKU, China
This book is designed for a professional audience who wish to learn about state-of-the-art AIS and AIS-based malware detection approaches.