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Detecting botnet by using particle swarm optimization algorithm based on voting system
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DOI：10.1016/j.future.2020.01.055.png)
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Abstract
En 中文
Botnets have recently been identified as serious Internet threats that are continually developing and expanding. Identifying botnets in the domain of network security is regarded as a new challenge and topic for research. There are several methods for detecting botnets in networks, and prior research has encountered problems, including a high error and inaccuracy in detection. In this paper, the botnet detection method by using a hybrid of particle swarm optimization (PSO) algorithm with a voting system (BD-PSO-V) was used to improve the challenges of previous studies. The PSO algorithm was employed to select outstanding and effective features in the detection of botnets. The voting system, including a deep neural network algorithm, support vector machine (SVM), and decision tree C4.5, were utilized to identify botnets and classify samples. The decision-making strategy of the voting system was based on maximum votes, and the most important innovation of this research was to combine the PSO feature selection algorithm with a voting system using deep learning to identify botnets. Two datasets, ISOT and Bot-IoT, were employed to further verify the BD-PSO-V system performance. BD-PSO-V simulation improved the accuracy by an average of similar to 0.42% and 0.17% in the ISOT dataset and the Bot-IoT dataset, respectively, compared to the other methods investigated. In addition, the effect of six well-known adversarial attacks on both datasets was evaluated. Despite a slight drop in accuracy rate, BD-PSO-V results had a promising performance against a variety of attacks. (C) 2020 Elsevier B.V. All rights reserved.
Keywords:
Botnet detection
PSO algorithm
Internet of things botnets
Voting system
Deep learning
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