Smart Hardware Trojan Detection System

Abstract

The IoT has become an indispensable part of human lives at work and home applications. Due to the need for an enormous number of IoT devices manufacturers are least concerned about security vulnerabilities during designing and developing of these devices. Because of this, it becomes easier for adversaries to manipulate the hardware and insert Trojans or Remote File Inclusion to control remotely. In this research, we aim to build a model to identify hardware Trojans in IoT devices using Deep learning. We used different machine learning models to evaluate the performance and accuracy. In addition we choose a distinctive feature that can detect the presence of Trojan in these devices. The proposed model is evaluated using an existing and real-time dataset generated using a smart city testbed, The testbed used was designed to simulate and evaluate the Hardware trojan attacks, and by using the real-time dataset we could measure the power profile and network traffic on the IoT gateway device to analyze the performance and the accuracy.