Artificial Immune System: Applications in Computer Security - Tapa dura

Acerca del autor

Ying Tan, PhD, is a Professor of Peking University, China. Dr. Tan is also the director of CIL@PKU. He serves as the editor-in-chief of International Journal of Computational Intelligence and Pattern Recognition, associate editor of IEEE Transactions on Cybernetics, IEEE Transactions on Neural Networks and Learning Systems, and International Journal of Swarm Intelligence Research, and also as an Editor of Springer’s Lecture Notes on Computer Science (LNCS). He is the founder and chair of the ICSI International Conference series. Dr. Tan is a senior member of the IEEE, ACM, and CIE. He has published over two-hundred papers in refereed journals and conferences in areas such as computational intelligence, swarm intelligence, data mining, and pattern recognition for information security.

De la contraportada

This book provides state-of-the-art information on the use, design, and development of the Artificial Immune System (AIS) and AIS-based solutions to computer security issues.

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.