Network Intrusions have become a pervasive threat to online ecosystems. Hence the need for an effective Intrusion Detection System (IDS) to safeguard and protect assets from a myriad of network attacks. A number of IDS that utilize effective feature selection methods have been proposed in the literature. However, this study asserts that an IDS can provide better performance if parameter optimization for classifier is embedded in the feature selection process. Consequently, this paper proposes a hybrid wrapper feature selection approach that combines Binary Bat algorithm with Lévy flights, together with Bat algorithm and Support Vector machines (BBAL-BA-SVM). The Binary Bat algorithm with Lévy flight performs the feature selection while the Bat algorithm performs parameter optimization on the SVM for each feature subset. Experimental results using NSL-KDD dataset prove that the proposed model provides higher accuracy in attack detection with lower false alarm rate over compared models.

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