*Article* **Intelligent Electromagnetic Sensors for Non-Invasive Trojan Detection**

**Ethan Chen \*, John Kan, Bo-Yuan Yang, Jimmy Zhu and Vanessa Chen**

> Department of Electrical and Computer Engineering, Carnegie Mellon University, Pittsburgh, PA 15213, USA; johnkan@andrew.cmu.edu (J.K.); boyuany@andrew.cmu.edu (B.-Y.Y.); jzhu@cmu.edu (J.Z.); vanessachen@cmu.edu(V.C.)

**\*** Correspondence: ethanchen@cmu.edu

**Abstract:** Rapid growth of sensors and the Internet of Things is transforming society, the economy and the quality of life. Many devices at the extreme edge collect and transmit sensitive information wirelessly for remote computing. The device behavior can be monitored through side-channel emissions, including power consumption and electromagnetic (EM) emissions. This study presents a holistic self-testing approach incorporating nanoscale EM sensing devices and an energy-efficient learning module to detect security threats and malicious attacks directly at the front-end sensors. The built-in threat detection approach using the intelligent EM sensors distributed on the power lines is developed to detect abnormal data activities without degrading the performance while achieving good energy efficiency. The minimal usage of energy and space can allow the energy-constrained wireless devices to have an on-chip detection system to predict malicious attacks rapidly in the front line.

**Keywords:** hardware security; electromagnetic sensing; machine learning; real time
