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Detecting Hardware Trojans using Machine Learning

State-of-the-art measurement technology and extremely fast clustering algorithm


In our modern society, literally billions of electronic devices are used every day. In the future, this number will rise dramatically with the expansion of the Internet of Things (IoT). Accompanying this increase is the growing cybersecurity threat of hardware Trojans embedded in semiconductor chips for malicious purposes. With the increase in the outsourcing of circuit design, manufacturing, and the use of IP from outside suppliers, the risks from hardware Trojans are on the rise. Deploying devices with these vulnerabilities could put our society at huge risk, especially if they impact critical systems like e-commerce encryption, autonomous driving vehicles, or aviation controllers. Since it is essential  that these systems are free of any malicious circuitry, the ability to detect hardware Trojans in electronic systems is extremely important. A research team headed by Nozomi Togawa, a professor at Waseda University’s Faculty of Science and Engineering, who have been researching hardware Trojan detection, used Keysight’s CX3300A Device Current Waveform Analyzer to dramatically improve their Trojan detection capabilities. The CX3300 has state-of-the-art dynamic current measurement technology capable of capturing hard-to-measure signatures at high bandwidth. It also supports an advanced machine learning algorithm capable of identifying small anomalies in very large databases (>1 Terabyte). In this article, we will describe how these technologies improved Trojan detection.

Trojan detection challenges

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BiS Team

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