STAKE: Secure Tracing of Anomalies using previous Knowledge and Extensions

By Rui Claro / 11-04-2021 / In categories Results

result, thesis

Translations: EN

STAKE: Secure Tracing of Anomalies using previous Knowledge and Extensions

Internet-of-things, Intrusion Detection System, Anomaly Detection, Machine Learning


Kevin B. Corrales


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Resumo (em inglês)

Internet of Things (IoT) devices have become more present in our households because of an increase in availability and affordability. However, a Smart Home is a challenging environment. It involves people engaged with devices for a variety of purposes, and it is difficult to detect anomalies that can be cyber attacks. In this dissertation we introduce STAKE, a Smart Home gateway for capturing and analyzing network traffic, with different levels of detail. The system supports anomaly detection plug-ins to spot attacks in near real-time. We evaluated our system with Machine Learning plug-ins based on the Elliptic Envelope and the Random Forest models. STAKE was able to execute different plug-ins and both detected anomalies.

As expected, each model returned different results. Both plug-ins demonstrated promising results when they were created. The Elliptic Envelope model obtained 93,9% accuracy and the Random Forest obtained 96,7%. However, when re-trained in the system the plug-ins did not demonstrate being flexible enough to changes in the Smart Home network. The Elliptic Envelope plug-in demonstrated tendency to produce excessively high false positive rates, which deteriorated the difference between benign and anomaly samples in the training data. The Random Forest plug-in demonstrated a exceedingly tendency for overfitting when re-trained in this kind of environment.