SAfER: Simplified Auto-encoder for (Anomalous) Event Recognition
Yuvin Perera, Gustavo Batista, Wen Song Hu, Salil S. Kanhere, Sanjay Kumar Jha · 2024
The IoT device marketplace has seen a large boom over the past decade. Myriad tech companies introduce IoT products to the market to capitalize on this demand. However, most IoT products have hidden security vulnerabilities, and these back-doors could be exploited by adversaries, which could lead to privacy-sensitive data leakage, unauthorized access or even physical harm to IoT device users. The event spoofing attack is one such attack where the adversary compromises one IoT device in the smart home ecosystem and sends spoofed event updates to cause undesired and unexpected outcomes of other IoT devices. Fortunately, previous research in this area has shown that it is possible to identify these attacks by analyzing the sensor readings of IoT devices to validate the status updates of other IoT devices. This paper proposes a novel Recurrent Neural Network based auto-encoder architecture that performs better than the current state-of-the-art in terms of detection accuracy, false positive rate and training time. In addition, the auto-encoder output is easily interpretable; therefore, subsequent identification of misbehaving IoT devices is more accessible for cybersecurity analysts.