Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Artficial Immune Systems and Their Applications
Artficial Immune Systems and Their Applications
Artificial Immune Systems: A New Computational Intelligence Paradigm
Artificial Immune Systems: A New Computational Intelligence Paradigm
CDIS: Towards a Computer Immune System for Detecting Network Intrusions
RAID '00 Proceedings of the 4th International Symposium on Recent Advances in Intrusion Detection
Coverage and Generalization in an Artificial Immune System
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
An Evolutionary Immune Network for Data Clustering
SBRN '00 Proceedings of the VI Brazilian Symposium on Neural Networks (SBRN'00)
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
An Immunological Approach to Change Detection: Algorithms, Analysis and Implications
SP '96 Proceedings of the 1996 IEEE Symposium on Security and Privacy
Architecture for an Artificial Immune System
Evolutionary Computation
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Revisiting LISYS: parameters and normal behavior
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
An artificial immune system architecture for computer securityapplications
IEEE Transactions on Evolutionary Computation
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
Is negative selection appropriate for anomaly detection?
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Applicability issues of the real-valued negative selection algorithms
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Revisiting Negative Selection Algorithms
Evolutionary Computation
Discriminating self from non-self with finite mixtures of multivariate Bernoulli distributions
Proceedings of the 10th annual conference on Genetic and evolutionary computation
A Novel Biology-Inspired Virus Detection Model with RVNS
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
Foundations of r-contiguous matching in negative selection for anomaly detection
Natural Computing: an international journal
Review: The use of computational intelligence in intrusion detection systems: A review
Applied Soft Computing
Phase transition and the computational complexity of generating r-contiguous detectors
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Evolving boundary detector for anomaly detection
Expert Systems with Applications: An International Journal
On permutation masks in hamming negative selection
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
WCIS: a prototype for detecting zero-day attacks in web server requests
LISA'11 Proceedings of the 25th international conference on Large Installation System Administration
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Negative selection algorithm is one of the most widely used techniques in the field of artificial immune systems. It is primarily used to detect changes in data/behavior patterns by generating detectors in the complementary space (from given normal samples). The negative selection algorithm generally uses binary matching rules to generate detectors. The purpose of the paper is to show that the low-level representation of binary matching rules is unable to capture the structure of some problem spaces. The paper compares some of the binary matching rules reported in the literature and study how they behave in a simple two-dimensional real-valued space. In particular, we study the detection accuracy and the areas covered by sets of detectors generated using the negative selection algorithm.