Improving the Accuracy of Network Intrusion Detection System in Medical IoT Systems through Butterfly Optimization Algorithm
Li Y. Ghoreishi S.-M. Issakhov A.
October 2022Springer
Wireless Personal Communications
2022#126Issue 31999 - 2017 pp.
Network intrusion detection systems analyze traffic in a medical IoT system to detect abnormal behaviors. Machine learning and artificial intelligence (AI) algorithms are widely used in designing intrusion detection systems to prevent attacks on a medical IoT system. In this paper, an artificial neural network is employed to detect abnormal behavior in a medical IoT system. The accuracy of the detection depends heavily on the features that are fed into the artificial neural network. Selecting the important and discriminative features of network traffic is a crucial and challenging issue because it has a significant impact on the learning process. In the proposed method, the butterfly optimization algorithm which is a meta-heuristic optimization algorithm is employed to select the optimal features for the learning process in an artificial neural network. The results achieved, 93.27% accuracy, indicate the capability of the butterfly optimization algorithm to determine discriminative features of network traffic data. The proposed algorithm outperformed the decision tree, support vector machine, and ant colony optimization, which was proposed in previous researches for the same goal.
Artificial intelligence , Butterfly optimization algorithm , Intrusion detection system , Medical IoT system , Meta-heuristic algorithm
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Department of Information, The Fifth Affiliated Hospital of Jinan University, Guangdong, Heyuan, China
Department of Electrical and Computer Engineering, Technical and Vocational University, Mazandaran, Sary, 4816831167, Iran
Al-Farabi Kazakh National University, Almaty, Kazakhstan
Kazakh-British Technical University, Almaty, Kazakhstan
Department of Information
Department of Electrical and Computer Engineering
Al-Farabi Kazakh National University
Kazakh-British Technical University
10 лет помогаем публиковать статьи Международный издатель
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