A Novel Anomaly-based Intrusion Detection System using Whale Optimization Algorithm WOA-Based Intrusion Detection System

Document Type : Original Article


1 Department of Computer Sciences, Faculty of Sciences, Golestan University, Gorgan, Iran

2 Department of Computer Sciences, Faculty of Sciences, Golestan University


The Internet has become an important part of many people’s daily activities. Therefore, numerous attacks threaten Internet users. IDS is a network intrusion detection tool used to quickly identify and categorize intrusions, attacks, or security issues in network-level and host-level infrastructure. Although much research has been done to improve IDS performance, many key issues remain. IDSs need to be able to more accurately detect different types of intrusions with fewer false alarms and other challenges. In this paper, we attempt to improve the performance of IDS using Whale Optimization Algorithm (WOA). The results are compared with other algorithms. NSL-KDD dataset is used to evaluate and compare the results. K-means clustering was chosen for pre-processing after a comparison between some of the existing classifier algorithms. The proposed method has proven to be a competitive method in terms of detection rate and false alarm rate base on a comparison with some of the other existing methods.


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 Aliakbar Tajari Siahmarzkooh received the B.Sc. degree in Computer Engineering from Ferdowsi University of Iran in 2009, and the M.Sc. and Ph.D. degree in Computer Science from University of Tabriz, Iran in 2012 and 2017, respectively. He has been working with the Department of Computer Sciences, Golestan University, since 2017, where he is now an assistant professor. His current research interests include network security, data mining and artificial intelligence.


 Mohammad Alimardani received the B.Sc. degree in Computer Sciences from Golestan University of Iran in 2021. He has been working with the Department of Computer Sciences, Golestan University on data mining approaches since 2019. His current research interests include network, data mining and cloud computing.