Real-Valued Negative Selection Algorithm with Variable-Sized Self Radius

Zeng, Jinquan; Tang, Weiwen; Liu, Caiming; Hu, Jianbin; Peng, Lingxi

doi:10.1007/978-3-642-34062-8_30

Cited by 2 publications

(2 citation statements)

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“…The authors in [20] simultaneously applied both binary and real valued detectors. Unlike the v-detector which uses variablesized detectors, some researchers [21] [22] have tried to use self-samples with variable sizes. The work presented in [22] introduced a new scheme termed VSRNSA to represent the self-space.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike the v-detector which uses variablesized detectors, some researchers [21] [22] have tried to use self-samples with variable sizes. The work presented in [22] introduced a new scheme termed VSRNSA to represent the self-space. In VSRNSA self-samples have variable-sized radiuses which are based on the total distance of every sample to other self-samples.…”

Section: Introductionmentioning

confidence: 99%

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Distribution estimation based negative selection algorithm (DENSA)

Osareh

Shadgar

2015

2015 International Workshop on Artificial Immune Systems (AIS)

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The Negative Selection Algorithm is an anomaly detection technique, inspired by the self-nonself discrimination behavior observed in the Biological Immune Systems. The most controversial problem of the negative selection algorithm is its inherent limitation in detecting foreign patterns as anomalies. This limitation causes high false positive rate in anomaly detection systems which are based on the negative selection algorithm. To tackle this limitation, this paper introduces an efficient negative selection algorithm by focusing on generating more efficient detectors using a more flexible boundary for selfpatterns. In other words, instead of applying conventional affinity measures, a Gaussian Mixture Model is fitted on normal space so that detectors are generated employing this Gaussian Mixture Model of self-space. The efficiency of the proposed algorithm is evaluated using different data sets including 2D synthesis data sets and NSL-KDD data set. The results indicate that generating detectors based on the Gaussian mixture model leads to more efficient negative selection algorithm even in real application with high dimensions where the traditional negative selection algorithms have limitations.

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