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A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm

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Abstract

The Internet of Things (IoT) is an emerging network that has experienced a lot of progress due to extensive developments and development in this network. Due to the increased number of IoT devices, it is endangered by a botnet-type attack. Many intrusion detection systems do not have the necessary and sufficient ability to detect and prevent such attacks. Intrusion detection systems (IDSs) have provided various promising solutions to discover malicious patterns and deal with botnet attacks. Many researchers have studied the effect of reducing the number of features of datasets on the detection performance of IoT attacks. Selecting several features from a dataset is a data mining technique effectively integrated into botnet detection and identification systems design. This paper presents a new botnet detection system to detect botnets in IoT using the feature selection technique based on the slime mold algorithm (SMA) and salp swarm algorithm (SSA). On the other hand, the number of features and the importance of features selected from the dataset can directly impact the detection error rate. Therefore, we have presented a new practical and efficient multi-objective algorithm for detecting botnets based on feature selection. To maximize the performance of the proposed algorithm, we have used chaos theory. In addition, we have integrated the mechanism of the Disruption operator with the proposed algorithm. An excellent balance is created in the components of exploration and exploitation. Finally, to check and evaluate the proposed algorithm, the standard datasets available in the UCI source, created based on the real traffic of infected devices on the IoT botnet, have been used. The results obtained from the proposed algorithm indicate a significant and good performance. The results show that it has a high ability to detect botnets in IoT networks and has been able to achieve an excellent low error rate.

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  1. Department of Computer Engineering, Urmia Branch, Islamic Azad University, Urmia, Iran

    Fatemeh Hosseini, Farhad Soleimanian Gharehchopogh & Mohammad Masdari

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  1. Fatemeh Hosseini
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  2. Farhad Soleimanian Gharehchopogh
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  3. Mohammad Masdari
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Correspondence to Farhad Soleimanian Gharehchopogh.

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Hosseini, F., Gharehchopogh, F.S. & Masdari, M. A Botnet Detection in IoT Using a Hybrid Multi-objective Optimization Algorithm. New Gener. Comput. 40, 809–843 (2022). https://doi.org/10.1007/s00354-022-00188-w

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