Communications of the ACM
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Immunocomputing: Principles and Applications
Immunocomputing: Principles and Applications
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An innate immune system for the protection of computer networks
WISICT '05 Proceedings of the 4th international symposium on Information and communication technologies
Architecture for an Artificial Immune System
Evolutionary Computation
An immunological approach to change detection: algorithms, analysis and implications
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
A sense of self for Unix processes
SP'96 Proceedings of the 1996 IEEE conference on Security and privacy
| Hi-index | 0.00 |
The paper extends the intrusion detection methodology proposed by Tarakanov et al. in [8] to k-dimensional shape spaces, for k greater or equal 2. k real vectors, representing antibodies, are used to recognize malicious (or, non-self) connection logs. We suggest a method for recognizing antigens (generating such antibodies) via Singular Value Decomposition of a real-valued matrix obtained by preprocessing a database of connection logs [9]. New incoming connection requests are recognized by the antibodies as either self (normal request), or non-self (potential attack), by (a) mapping them into a k-dimensional shape space, and (b) evaluating the minimum Hamming distance between their image and that of a known attack logs. It is easy to see that using a shape space of dimension greater than 2 significantly reduces false positives.