This study describes our experience in detecting network scanning of IoT devices through energy consumption analysis. We carried out a set of experiments where five benign activities (web requests, updates, file transfers, audio streaming, and video streaming) and one malicious activity (Nmap network scanning) were considered. Energy traces were collected using a high-accuracy power monitor. Machine learning classification using a Neural Network achieved 97.14% accuracy in identifying the malicious scanning activity. Results demonstrate that energy-based detection can effectively distinguish between normal operations and cyberattacks, offering a promising, passive security approach for IoT devices.
Detection of Network Scanning in the Internet of Things through Energy Consumption Analysis
Ottali, Tommaso;Perazzo, Pericle;Vecchio, Alessio
2025-01-01
Abstract
This study describes our experience in detecting network scanning of IoT devices through energy consumption analysis. We carried out a set of experiments where five benign activities (web requests, updates, file transfers, audio streaming, and video streaming) and one malicious activity (Nmap network scanning) were considered. Energy traces were collected using a high-accuracy power monitor. Machine learning classification using a Neural Network achieved 97.14% accuracy in identifying the malicious scanning activity. Results demonstrate that energy-based detection can effectively distinguish between normal operations and cyberattacks, offering a promising, passive security approach for IoT devices.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
