Physical Layer Anomaly Detection Mechanisms in IoT Networks
Pedro Martins, André B. Reis, Paulo Salvador, Susana Sargento · 2020
With the advent of IoT and wireless mesh networks, security risks inherent to these types of networks have grown in number, severity, and potential for harm. Most of the approaches currently available for anomaly detection in IoT networks perform frame and packet inspection, which may inadvertently reveal private behavioural patterns of its users.This paper proposes a privacy-focused framework for anomaly detection which analyses radio activity at the physical layer, measuring silence and activity periods, and extracts relevant features for training One-Class Classification (OCC) models.We train our models with data captured from interactions with an Amazon Echo with multiple devices generating background noise, thus simulating a business-like environment, and test them against a similar scenario with a tampered network node periodically uploading data to a local machine. Our data show that the best performing model is able to detect anomalies with a 99% precision rate.