Intrusion detection in networks using crow search optimization algorithm with adaptive neuro-fuzzy inference system

Intrusion detection system has become the fundamental part for the network security and essential for network security because of the expansion of attacks which causes many issues. This is because of the broad development of internet and access to data systems around the world. For detecting the abnormalities present in the network or system, the intrusion detection system (IDS) is used. Because of the large volume of data, the network gets expanded with false alarm rate of intrusion and detection accuracy decreased. This is one of the significant issues when the network experiences unknown attacks. The principle objective was to expand the accuracy and reduce the false alarm rate (FAR). To address the above difficulties the proposed with Crow Search Optimization algorithm with Adaptive Neuro-Fuzzy Inference System (CSO-ANFIS) is used. The ANFIS is the combination of fuzzy interference system and artificial neural network, and to enhance the performance of the ANFIS model the crow search optimization algorithm is used to optimize the ANFIS. The NSL-KDD data set was used to validate the performance of intrusion detection of the proposed model and the experiment results are compared with other existing techniques for overall performance validation. The results of the intrusion detection based on the NSLKDD dataset was better and efficient compared with those models because the detection rate was 95.80% and the FAR result was 3.45%.