The objective of this project is to develop a lightweight malware detection system tailored for IoT environments. The system monitors IoT device behavior and detects anomalies indicating malware infections while ensuring minimal computational overhead suitable for resource-constrained devices.
Study IoT architecture, communication protocols (MQTT, HTTP), and device constraints.
Research common IoT malware such as botnets targeting smart devices.
Design a lightweight monitoring framework to collect device activity logs.
Extract behavioral features such as unusual traffic spikes or repeated connection attempts.
Implement anomaly detection algorithms suitable for low-power devices.
Develop threshold-based alerts for suspicious activity.
Store logs securely for further forensic analysis.
Create a dashboard to monitor IoT network health.
Test the system using simulated IoT malware scenarios.
Measure system performance in terms of resource usage and detection accuracy.
Document scalability challenges and possible improvements.
