Incorporating evolutionary computation for securing wireless network against cyberthreats
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Incorporating evolutionary computation for securing wireless network against cyberthreats Shubhra Dwivedi1 · Manu Vardhan1 · Sarsij Tripathi1
© Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Due to the rapid growth of internet services, the demand for protection and security of the network against sophisticated attacks is continuously increasing. Nowadays, in network security, an intrusion detection system (IDS) plays an important role to detect intrusive activity. With the purpose of reducing the search dimensionality and enhancing classification performance of IDS model, in the literature several hybrid evolutionary algorithms have been investigated to tackle anomaly detection problems, but they have few drawbacks such as poor diversity, massive false negative rate, and stagnation. To resolve these limitations, in this study, we introduce a new hybrid evolutionary algorithm combining the techniques of grasshopper optimization algorithm (GOA) and simulated annealing (SA), called GOSA for IDS that extracts the most noteworthy features and eliminates irrelevant ones from the original IDS datasets. In the proposed method, SA is integrated into GOA, while utilizing it to increase the solution quality after each iteration of GOA. Support vector machine is used as a fitness function in the proposed method to select relevant features which can help to classify attacks accurately. The performance of the proposed method is evaluated on two IDS datasets such as NSL-KDD and UNSW-NB15. From experimental results, we observe that the proposed method outperforms existing state-of-the-art methods and attains high detection rate as 99.86%, an accuracy as 99.89%, and low false alarm rate as 0.009 in NSL-KDD and high detection rate as 98.85%, an accuracy as 98.96%, and low false alarm rate as 0.084 in UNSW-NB15. Keywords Intrusion detection system · Simulated annealing · Evolutionary algorithms · Grasshopper optimization algorithm
* Shubhra Dwivedi [email protected] 1
Department of Computer Science and Engineering, NIT Raipur, Raipur, Chhattisgarh, India
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1 Introduction Nowadays, the world has perceived a significant evolution in the different areas of wireless technologies such as Bluetooth, ZigBee, Satellite, NFC, Wi-Fi, WiMAX, and 5G communication. Therefore, in recent years, wireless network security is one of the fastest growing areas of research. As reported by Cisco [1], it is expected that the number of wireless/wired IP devices will be three times more than worldwide population in 2022, producing 4.8 ZB of data each year. The massive growth of data that are transmitted through with variety of devices and internet protocols have raised serious security concerns, which have increased the importance of developing advanced intrusion detection systems (IDSs). Since prevention techniques are never enough, intrusion detection systems, which monitor system activities and detect intrusions, are generally used to balance other security me
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