Abstract
Today, the never-ending stream of security threats requires new security solutions capable to deal with large data volumes and high speed network connections in real-time. Intrusion Detection Systems are an omnipresent component of most security systems and may offer a viable answer. In this paper we propose a network anomaly IDS which merges the Support Vector Machines classifier with an improved version of the Bat Algorithm (BA). We use the Binary version of the Swarm Intelligence algorithm to construct a wrapper feature selection method and the standard version to elect the input parameters for SVM. Tests with the NSL-KDD dataset empirically prove our proposed model outperforms simple SVM or similar approaches based on PSO and BA, in terms of attack detection rate and false alarm rate generated after fewer number of iterations.
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