Botnets and Critical Infrastructure Security: A Survey
Jaramogi Oginga Odinga University of Science and Technology.
Review Article
GSC Advanced Research and Reviews, 2025, 22(01), 330-361.
Article DOI: 10.30574/gscarr.2025.22.1.0445
Publication history:
Received on 10 October 2024; revised on 22 January 2025; accepted on 25 January 2025
Abstract:
Botnets have emerged as a significant threat to the security and resilience of critical infrastructure systems. These decentralized networks of compromised devices enable malicious actors to execute sophisticated cyberattacks, such as Distributed Denial of Service (DDoS) attacks, data exfiltration, and ransomware deployment, which can disrupt essential services and compromise national security. This paper examines the evolving landscape of botnet threats to critical infrastructure, highlighting the vulnerabilities inherent in increasingly interconnected systems, including industrial control systems (ICS), smart grids, and healthcare networks. It explores how advancements in artificial intelligence (AI), machine learning (ML), and the Internet of Things (IoT) have expanded the attack surface for botnets while also offering potential mitigation strategies. Furthermore, the paper reviews contemporary defense mechanisms, including anomaly detection, threat intelligence sharing, and network segmentation, and assesses their efficacy in safeguarding critical infrastructure. By identifying gaps in existing security frameworks and proposing a multi-layered, proactive defense approach, this study aims to enhance the resilience of critical infrastructure against botnet-driven threats. The findings underscore the urgent need for collaboration between policymakers, industry stakeholders, and cybersecurity experts to develop robust and adaptive solutions in the face of this escalating cyber threat.
Keywords:
Bots; Botnet; Security; Critical Infrastructure; Performance
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Copyright © 2025 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0
