Intelligent Fingerprinting to Detect Data Leakage Attacks on Spectrum Sensors

Abstract

Data confidentiality protection is a must for IoT and crowdsensing platforms, and a challenge due to the constrained nature of their sensors. Currently, the combination of device fingerprinting and anomaly detection systems based on Machine and Deep Learning (ML/DL) techniques is one of the most promising approaches to detect zero-day cyberattacks. However, most of existing work is not suitable for resource-constrained devices or does not deal with cyberattacks affecting data confidentiality of spectrum sensors. Thus, this paper proposes a framework that monitors network interface events of sensors, uses unsupervised learning to create fingerprints, and detects anomalies produced by such cyberattacks. The framework validation has been performed in the crowdsensing platform ElectroSense, where a sensor has been infected by a backdoor leaking different sensitive data during an experiment. A set of unsupervised learning algorithms has been evaluated, being Autoencoder the one showing the best balance when detecting normal behavior and data leakages of different sizes and at frequencies, while providing a reduced detection time and sensor resources consumption.