Enhanced Peer-to-Peer Botnet Detection Using Differential Evolution for Optimized Feature Selection

Sangita Baruah, Vaskar Deka, Dulumani Das, Utpal Barman and Manob Jyoti Saikia ()
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Sangita Baruah: Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA
Vaskar Deka: Department of Information Technology, Gauhati University, Guwahati 781014, India
Dulumani Das: Faculty of Computer Technology, Assam Down Town University, Guwahati 781026, India
Utpal Barman: Department of Information Technology, Assam Skill University, Mangaldoi 784125, India
Manob Jyoti Saikia: Biomedical Sensors & Systems Lab, University of Memphis, Memphis, TN 38152, USA

Future Internet, 2025, vol. 17, issue 6, 1-27

Abstract: With the growing prevalence of cybercrime, botnets have emerged as a significant threat, infiltrating an increasing number of legitimate computers annually. Challenges arising for organizations, educational institutions, and individuals as a result of botnet attacks include distributed denial of service (DDoS) attacks, phishing attacks, and extortion attacks, generation of spam, and identity theft. The stealthy nature of botnets, characterized by constant alterations in network structures, attack methodologies, and data transmission patterns, poses a growing difficulty in their detection. This paper introduces an innovative strategy for mitigating botnet threats. Employing differential evolution, we propose a feature selection approach that enhances the ability to discern peer-to-peer (P2P) botnet traffic amidst evolving cyber threats. Differential evolution is a population-based meta-heuristic technique which can be applied to nonlinear and non-differentiable optimization problems owing to its fast convergence and use of few control parameters. Apart from that, an ensemble learning algorithm is also employed to support and enhance the detection phase, providing a robust defense against the dynamic and sophisticated nature of modern P2P botnets. The results demonstrate that our model achieves 99.99% accuracy, 99.49% precision, 98.98% recall, and 99.23% F1-score, which outperform the state-of-the-art P2P detection approaches.

Keywords: P2P botnet; feature selection; metaheuristic algorithm; differential evolution; DEAP library (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2025
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