Towards a Lightweight and Efficient Gaussian Mixture Model for Detecting Mirai Botnet Attacks in IoT Environments

Internet of Things (IoT) devices are increasingly susceptible to botnet threats, with Mirai-based attacks posing significant security challenges. To address these concerns, we present a lightweight anomaly detection model based on a Gaussian Mixture Model (GMM), tailored for detecting Mirai botnet traffic. By leveraging anomaly detection techniques, our approach identifies malicious activity with optimized latency and computational efficiency, making it suitable for resource-constrained IoT environments. Trained specifically on Mirai-related traffic, the proposed model achieves comparable detection accuracy while significantly improving latency compared to traditional decision tree-based models. These results underscore the effectiveness of the GMM approach in balancing detection performance and real-time responsiveness for IoT applications.