Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
A Sense of Self for Unix Processes
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Anomaly Detection Using Real-Valued Negative Selection
Genetic Programming and Evolvable Machines
Architecture for an Artificial Immune System
Evolutionary Computation
How Do We Evaluate Artificial Immune Systems?
Evolutionary Computation
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Revisiting LISYS: parameters and normal behavior
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Revisiting Negative Selection Algorithms
Evolutionary Computation
Intrusion detection using sequences of system calls
Journal of Computer Security
Application areas of AIS: the past, the present and the future
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Review Article: Recent Advances in Artificial Immune Systems: Models and Applications
Applied Soft Computing
Clonal selection algorithm for classification
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
Hi-index | 0.00 |
One of the major challenges for negative selection is to efficiently generate effective detectors. The experiment in the past shows that random generation fails to generate useful detectors within acceptable time duration. In this paper, we propose an antigen feedback mechanism for generating the detectors. For an unmatched antigen, we make a copy of the antigen and treat it the same as a newly randomly generated antibody: it goes through the same maturing process and is subject to elimination due to self matching. If it survives and is then activated by more antigens, it becomes a legitimate detector. Our experiment demonstrates that the antigen feedback mechanism provides an efficient way to generate enough effective detectors within a very short period of time. With the antigen feedback mechanism, we achieved 95.21% detection rate on attack strings, with 4.79% false negative rate, and 99.21% detection rate on normal strings, 0.79% false positive. In this paper, we also introduce Arisytis --- Artificial Immune System Tool Kits--- a project we are undertaking for not only our own experiment, but also the research communities in the same area to avoid the waste on repeatedly developing similar software. Arisytis is available on the public domain. Finally, we also discuss the effectiveness of the r-continuous bits match and its impact on data presentation.