Enhancing Botnet Detection with Explainable AI and OSINT

Computer Science and Engineering

Botnets, which are networks of compromised computers controlled by attackers, pose a serious cybersecurity threat. Detecting these threats becomes more challenging when cybercriminals use Domain Generation Algorithms (DGAs) to create random domain names for command-and-control servers, allowing them to bypass traditional security measures. Conventional detection methods, such as signature-based, rule-based, and heuristic approaches, struggle against adaptive and evolving DGAs due to their lack of flexibility. To address this issue, this research focuses on enhancing botnet DGA detection using Explainable AI (XAI) and Open-Source Intelligence (OSINT). XAI improves transparency by providing insights into how threats are detected, enabling cybersecurity professionals to understand and trust AI-driven systems. OSINT promotes real-time intelligence sharing, allowing organizations to collaborate and strengthen their defenses against emerging threats. As cybercriminals continuously refine their attack strategies, traditional security systems become less effective. Implementing advanced, explainable, and cooperative security solutions is essential for staying ahead of evolving cyber threats. By leveraging XAI for interpretability and OSINT for collaborative defense, cybersecurity professionals can improve detection accuracy and enhance overall threat mitigation strategies.

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