<![CDATA[The widespread adoption of Internet of Things (IoT) devices in smart homes, health care, and Industry 4.0 has brought new security challenges, especially given the growing complexity of malware and botnets threats. Conventional signature-based approaches are not always suitable for IoT device deployments due to device diversity, resource constraints, and the rise of zero-day attacks. The research introduces a multi-faceted approach to malware detection, combining signature-based methods, anomaly detection, and machine learning for better accuracy and timely detection. Data was captured from an IoT testbed comprising smart cameras, sensors and embedded devices. A dataset of 50,000 labeled network flow records was created with Wireshark and Snort, preprocessed, and then features were extracted. A Random Forest classifier was developed and combined with YARA-based signature matching and Z-score behavioral analysis, to create a hybrid detection system. The model was tested on a 7,500-sample test set, as well as in a 48-hour real-time IoT deployment trial. The testing results show that the hybrid system we propose has an accuracy of 97.4%, precision of 95.6%, recall of 96.8%, and an F1-score of 96.2%, with a false positive rate of 2.3%. The real-time test achieved 97% detection rate with an average decision time of 0.85 seconds. The system also achieved 92.1% accuracy with adversarial attacks using modified and new malicious samples. These results demonstrate that hybrid approaches using machine learning, signature analysis and behavioral analysis are effective in improving IoT malware detection. Our lightweight hybrid approach offers a lightweight, scalable and effective solution for IoT devices with limited computational power, and remains robust against emerging threats.]]>
