| Title | Hardware Trojan Detection Based on SRC |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Sun, Chen, Cheng, Liye, Wang, Liwei, Huang, Yun |
| Conference Name | 2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC) |
| Keywords | composability, cyber physical security, cyber physical systems, Hardware, hardware trojan, integrated circuits, machine learning, Noise measurement, pubcrawl, resilience, Resiliency, security, Size measurement, sparse representation-based classifier (SRC), supply chain security, Training, trojan horse detection, Trojan horses |
| Abstract | The security of integrated circuits (IC) plays a very significant role on military, economy, communication and other industries. Due to the globalization of the integrated circuit (IC) from design to manufacturing process, the IC chip is vulnerable to be implanted malicious circuit, which is known as hardware Trojan (HT). When the HT is activated, it will modify the functionality, reduce the reliability of IC, and even leak confidential information about the system and seriously threatens national security. The HT detection theory and method is hotspot in the security of integrated circuit. However, most methods are focusing on the simulated data. Moreover, the measurement data of the real circuit are greatly affected by the measurement noise and process disturbances and few methods are available with small size of the Trojan circuit. In this paper, the problem of detection was cast as signal representation among multiple linear regression and sparse representation-based classifier (SRC) were first applied for Trojan detection. We assume that the training samples from a single class do lie on a subspace, and the test samples can be represented by the single class. The proposed SRC HT detection method on real integrated circuit shows high accuracy and efficiency. |
| DOI | 10.1109/YAC51587.2020.9337595 |
| Citation Key | sun_hardware_2020 |
Hardware Trojan Detection Based on SRC