A Systematic Literature Review (SLR) of Mirai Botnet Compromise Detection in Internet of Things (IoT) Network

Since its invention, Mirai botnet has remained a significant concern in IoT network security. The botnet and its evolving variants are a major threat to professionals responsible for securing IoT infrastructures. The danger of the botnet is attributed to the fact that it has been utilized for the execution of numerous Distributed Denial of Service (DDoS) attacks on different network infrastructures in the past. Several researchers have proposed techniques in mitigating the effect of this botnet. This research systematically reviews existing detection techniques and evaluates how effective they are in mitigating Mirai botnet attacks between 2017 and 2024. Using PRISMA methodology, 177 articles were initially identified from Scopus, Springer Link, IEEE Xplore, and Web of Science in order to broaden the scope of the search. 27 studies passed the inclusion criteria, and were analyzed thereafter. Findings reveal a predominant reliance on AI-driven detection methods, such as LSTM and ensemble models, which demonstrate higher accuracy and scalability when compared to traditional techniques. This review also compares threat intelligence platforms like AlienVault, CrowdStrike, and Recorded Future, to assess their contributions to dynamic detection frameworks. Finally, the study explores research gaps and proposes future directions for developing scalable real-time detection systems integrating multi-source threat feeds