IMPLEMENTATION OF A HARDWARE TROJAN CHIP DETECTOR MODEL USING ARDUINO MICROCONTROLLER

These days, hardware devices and its associated activities are greatly impacted by threats amidst of various technologies. Hardware trojans are malicious modifications made to the circuitry of an integrated circuit, Exploiting such alterations and accessing the level of damage to devices is considered in this work. These trojans, when present in sensitive hardware system deployment, tends to have potential damage and infection to the system. This research builds a hardware trojan detector using machine learning techniques. The work uses a combination of logic testing and power side-channel analysis (SCA) coupled with machine learning for power traces. The model was trained, validated and tested using the acquired data, for 5 epochs. Preliminary logic tests were conducted on target hardware device as well as power SCA. The designed machine learning model was implemented using Arduino microcontroller and result showed that the hardware trojan detector identifies trojan chips with a reliable accuracy. The power consumption readings of the hardware characteristically start at 1035-1040mW and the power time-series data were simulated using DC power measurements mixed with additive white Gaussian noise (AWGN) with different standard deviations. The model achieves accuracy, precision and accurate recall values. Setting the threshold proba¬bility for the trojan class less than 0.5 however increases the recall, which is the most important metric for overall accuracy acheivement of over 95 percent after several epochs of training

Side-channel based intrusion detection for industrial control systems

Industrial Control Systems are under increased scrutiny. Their security is historically sub-par, and although measures are being taken by the manufacturers to remedy this, the large installed base of legacy systems cannot easily be updated with state-of-the-art security measures. We propose a system that uses electromagnetic side-channel measurements to detect behavioural changes of the software running on industrial control systems. To demonstrate the feasibility of this method, we show it is possible to profile and distinguish between even small changes in programs on Siemens S7-317 PLCs, using methods from cryptographic side-channel analysis.Comment: 12 pages, 7 figures. For associated code, see https://polvanaubel.com/research/em-ics/code