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Particle Swarm Optimization Algorithm Based Artificial Neural Network for Botnet Detection

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Abstract

A standout amongst the most dangers to the cyber security is known as Botnet since it offers a conveyed stage for many undesirable activities. From the network traffic flow, the identification of Botnet is a fundamental test. Artificial Neural Network–Particle Swarm Optimization (ANN–PSO) based botnet discovery is proposed in this paper. In this paper, ISCX dataset is utilized for botnet location. The features are classified as botnet flow and normal flow by giving the features separated from the dataset as a contribution to the grouping. For grouping, we have displayed ANN–PSO which lessens the false classification ratio and time multifaceted nature to 3.3% and 14 s. We contrast our proposed work with other existing work and demonstrate that our work is superior to anything that of alternate works in the simulation results.

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Data Availability

The already existing algorithms data used to support the findings of this study have not been made available.

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  1. A.V.C. College (Autonomous), Mannampandal, Mayiladuthurai, 609305, India

    P. Panimalar

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  1. P. Panimalar
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Correspondence to P. Panimalar.

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Panimalar, P. Particle Swarm Optimization Algorithm Based Artificial Neural Network for Botnet Detection. Wireless Pers Commun 121, 2655–2666 (2021). https://doi.org/10.1007/s11277-021-08841-1

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  • DOI: https://doi.org/10.1007/s11277-021-08841-1

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