Intelligent Feature Selection Method rooted in Binary Bat Algorithm for Intrusion Detection

The multitude of hardware and software applications generate a lot of data and burden security solutions that must acquire informations from all these heterogenous systems. Adding the current dynamic and complex cyber threats in this context, make it clear that new security solutions are needed. In this paper we propose a wrapper feature selection approach that combines two machine learning algorithms with an improved version of the Binary Bat Algorithm. Tests on the NSL-KDD dataset empirically prove that our proposed method can reduce the number of features with almost 60% and obtains good results in terms of attack detection rate and false alarm rate, even for unknown attacks.