An ai-enabled IoT intrusion detection system built on beagleboard hardware

Abstract

The present invention is related to an AI-enabled IoT intrusion detection system built on BeagleBoard hardware. The system captures real-time traffic, preprocesses it, and applies machine learning algorithms to classify it as Normal, Suspicious, or Attack. When malicious activity is detected, the system triggers real-time alerts through LED, buzzer, and optional mobile / email notifications. It is low-cost, explainable, and deployable in IoT networks, smart environments, and educational labs, thereby enhancing cybersecurity in resource-limited setup.