Abstract
The ability of Negative Selection Algorithm (NSA) to solve a number of anomaly detection problems has proved to be effective. This paper thus presents an experimental study of negative selection algorithm with some classification algorithms. The purpose is to ascertain their efficiency rates in accurately detecting abnormalities in a system when tested with well-known datasets. Negative selection algorithm with some selected immune and classifier algorithms are used for experimentation and analysis. Three different datasets have been acquired for this task and a comparison performance executed. The empirical results illustrates that the artificial immune system of negative selection algorithm can achieve highest detection and lowest false alarm. Thus, it signifies the suitability and potentiality of NSA for discovering unusual changes in normal behavioral flow.
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Lasisi, A., Ghazali, R., Herawan, T. (2014). Comparative Performance Analysis of Negative Selection Algorithm with Immune and Classification Algorithms. In: Herawan, T., Ghazali, R., Deris, M. (eds) Recent Advances on Soft Computing and Data Mining. Advances in Intelligent Systems and Computing, vol 287. Springer, Cham. https://doi.org/10.1007/978-3-319-07692-8_42
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DOI: https://doi.org/10.1007/978-3-319-07692-8_42
Publisher Name: Springer, Cham
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