Biblio

Cyber physical systems (CPS) is a dominant technology in today's world due to its vast variety of applications. But in recent times, the alarmingly increasing breach of privacy and security in CPS is a matter of grave concern. Security and trust of CPS has become the need of the hour. Hardware Trojans are one such a malicious attack which compromises on the security of the CPS by changing its functionality or denial of services or leaking important information. This paper proposes the detection of Hardware Trojans at the system level in AES-256 decryption algorithm implemented in Atmel XMega Controller (Target Board) using a combination of side-channel power analysis and machine learning. Power analysis is done with help of ChipWhisperer-Lite board. The power traces of the golden algorithm (Hardware Trojan free) and Hardware Trojan infected algorithms are obtained and used to train the machine learning model using the 80/20 rule. The proposed machine learning model obtained an accuracy of 97%-100% for all the Trojans inserted.

Intellectual property (IP) and integrated circuit (IC) piracy are of increasing concern to IP/IC providers because of the globalization of IC design flow and supply chains. Such globalization is driven by the cost associated with the design, fabrication, and testing of integrated circuits and allows avenues for piracy. To protect the designs against IC piracy, we propose a fingerprinting scheme based on side-channel power analysis and machine learning methods. The proposed method distinguishes the ICs which realize a modified netlist, yet same functionality. Our method doesn't imply any hardware overhead. We specifically focus on the ability to detect minimal design variations, as quantified by the number of logic gates changed. Accuracy of the proposed scheme is greater than 96 percent, and typically 99 percent in detecting one or more gate-level netlist changes. Additionally, the effect of temperature has been investigated as part of this work. Results depict 95.4 percent accuracy in detecting the exact number of gate changes when data and classifier use the same temperature, while training with different temperatures results in 33.6 percent accuracy. This shows the effectiveness of building temperature-dependent classifiers from simulations at known operating temperatures.