Lightweight Machine Learning-Based IDS for IoT Environments

This paper presents a lightweight, machine learning-based Intrusion Detection System (IDS) optimized for Internet of Things (IoT) environments, specifically designed to handle encrypted traffic. Utilizing advanced machine learning techniques, our IDS efficiently detects and analyzes malicious activities within encrypted data streams without decryption. Experimental results show that our system not only exceeds traditional IDS solutions in detection accuracy but also operates effectively under IoT constraints. Its capability to dynamically adapt to new threats and learn from continuous traffic ensures its suitability for the dynamic landscape of network security.