LE3D: A Lightweight Ensemble Framework of Data Drift Detectors for Resource-Constrained Devices

Abstract

Data integrity becomes paramount as the number of Internet of Things (IoT) sensor deployments increases. Sensor data can be altered by benign causes or malicious actions. Mechanisms that detect drifts and irregularities can prevent disruptions and data bias in the state of an IoT application. This paper presents LE3D, an ensemble framework of data drift estimators capable of detecting abnormal sensor behaviours. Working collaboratively with surrounding IoT devices, the type of drift (natural/abnormal) can also be identified and reported to the end-user. The proposed framework is a lightweight and unsupervised implementation able to run on resource-constrained IoT devices. Our framework is also generalisable, adapting to new sensor streams and environments with minimal online reconfiguration. We compare our method against state-of-the-art ensemble data drift detection frameworks, evaluating both the real-world detection accuracy as well as the resource utilisation of the implementation. Experimenting with real-world data and emulated drifts, we show the effectiveness of our method, which achieves up to 97% of detection accuracy while requiring minimal resources to run.

Cybersecurity in Motion: A Survey of Challenges and Requirements for Future Test Facilities of CAVs

“…Therefore, a single attack could exhibit multiple data flows across the data surface. In terms of potential CAV cyber-attacks that constitute the attack surface, an attacker may exploit vulnerabilities within the physical infrastructure (e.g., electric charging stations), perform adversarial modifications to sensor data [23,24], perform poisoning attacks to intelligent agents [25], or perform network attacks that disrupt communications (e.g., a Denial-of-Service (DoS) attack [26]). For a comprehensive overview of various attack points that constitute the CAV attack surface, we refer the reader to [13,14,27,28].…”