Anomaly network-based intrusion detection system using a reliable hybrid artificial bee colony and AdaBoost algorithms

Abstract

Intrusion detection systems (IDSs) has been considered as the main component of a safe network. One of the problems of these security systems is false alarm report of intrusion to the network and intrusion detection accuracy that happens due to the high volume of network data. This paper proposes a new reliable hybrid method for an anomaly network-based IDS (A-NIDS) using artificial bee colony (ABC) and AdaBoost algorithms in order to gain a high detection rate (DR) with low false positive rate (FPR). ABC algorithm is used to feature selection and AdaBoost are used to evaluate and classify the features. Results of the simulation on NSL-KDD and ISCXIDS2012 datasets confirm that this reliable hybrid method has a significant difference from other IDS, which are accomplished according to the same dataset. It has demonstrated differently better performance in different attacks-based scenarios. The accuracy and detection rate of this method has been improved in comparison with legendary methods. Keywords: Anomaly network-based, Intrusion detection system, Feature selection, Artificial bee colony, AdaBoost