Augmented Negative Selection Algorithm with Complete Random Subspace Technique for Anomaly Detection

Abstract

Negative selection algorithm is an important algorithm in the artificial immune system, inspired by the biological immune system. Traditional negative selection algorithms lack adaptive learning ability in high-dimensional space due to data sparsity and meaningless distance measurement. To solve these problems, an improved negative selection algorithm called Negative Selection Algorithm with Complete Random Subspace Technique (RS-NSA), is proposed in this paper. It adopts a bootstrap method to reduce the rate of misclassification resulting from the anomalies covered by the regions of normal samples. By using the complete random subspace technology, it reduces dimensionality to alleviate the curse of dimensionality. In addition, the ensemble learning technique is introduced to improve accuracy, in which component classifiers can be replaced by any negative selection algorithm. Empirical evaluation on UCI datasets reveals that, compared with V-detector, our proposed method can not only achieve a higher detection rate and a lower false alarm rate, but also shorten the training time.