Review
Version 1
Preserved in Portico This version is not peer-reviewed
Botnet Detection Techniques: A Comparative Study
Version 1
: Received: 5 November 2023 / Approved: 6 November 2023 / Online: 6 November 2023 (08:17:49 CET)
How to cite: Alauthman, M. Botnet Detection Techniques: A Comparative Study. Preprints 2023, 2023110311. https://doi.org/10.20944/preprints202311.0311.v1 Alauthman, M. Botnet Detection Techniques: A Comparative Study. Preprints 2023, 2023110311. https://doi.org/10.20944/preprints202311.0311.v1
Abstract
Abstract: Botnets pose a grave cybersecurity threat, enabling widescale malicious activities through networks of compromised devices. Detecting botnets is challenging given their frequent use of evasion techniques like encryption. Traditional signature-based methods fail against modern botnets capable of zero-day attacks. This paper surveys recent advances applying machine learning for botnet detection based on analysis of network traffic payloads, flows, DNS data, and hybrid feature fusion. Core machine learning models include support vector machines, neural networks, random forests, and deep learning architectures, which extract patterns to separate benign and botnet behaviors automatically. Results demonstrate machine learning's capabilities in identifying heterogeneous botnets using artefacts in network streams. However, challenges remain around limited labeled data, real-time streaming, adversarial evasion, and model interpretability. Promising directions involve semi-supervised learning, adversarial training, scalable analytics, and explainable AI to address these gaps. Beyond the technical aspects, responsible development and deployment of botnet detection systems raise ethical considerations around privacy, transparency, and accountability. With diligent cross-disciplinary collaboration, machine learning promises enhanced, generalizable, and trustworthy techniques to combat the serious threat posed by continuously evolving botnets across the digital ecosystem.
Keywords
Botnet detection; Network traffic analysis; Machine learning; Deep learning Cybersecurity; Adversarial machine learning
Subject
Computer Science and Mathematics, Computer Networks and Communications
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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