Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Use of chaotic sequences in a biologically inspired algorithm for engineering design optimization
Expert Systems with Applications: An International Journal
A negative selection algorithm for classification and reduction of the noise effect
Applied Soft Computing
Induction machine fault detection using clone selection programming
Expert Systems with Applications: An International Journal
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
Learning and optimization using the clonal selection principle
IEEE Transactions on Evolutionary Computation
An immunity-based technique to characterize intrusions in computernetworks
IEEE Transactions on Evolutionary Computation
Generating Compact Classifier Systems Using a Simple Artificial Immune System
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Chaos driven evolutionary algorithms for the task of PID control
Computers & Mathematics with Applications
Run-time malware detection based on positive selection
Journal in Computer Virology
Computers & Mathematics with Applications
Expert Systems with Applications: An International Journal
Hi-index | 12.05 |
This paper proposes a new negative selection algorithm method that uses chaotic maps for parameter selection. This has been done by using of chaotic number generators each time a random number is needed by the original negative selection for mutation and generation of initial population. The coverage of negative selection algorithm has been improved by using chaotic maps. The proposed algorithm utilizes from clonal selection to obtain optimal non-overlapping detectors. In many anomaly or fault detection systems, training data don't represent all normal data and self/non-self space often varies over the time. In the testing stage, when any test data cannot be detected by any self or non-self detector, the nearest detectors are found by K-Nearest Neighbor (K-NN) method and the nearest detector is mutated as a new detector to detect this new sample. Proposed chaotic-based hybrid negative selection algorithm (CHNSA) has been analyzed in the broken rotor bar fault detection and Fisher Iris datasets.