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S&P2020顶会

Detection of Electromagnetic Interference Attacks on Sensor Systems

Youqian Zhang, Kasper Rasmussen

2020年份
68被引次数
8顶会引用

摘要

Sensor systems are used every time a microcontroller needs to interact with the physical world. They are abundant in home automation, factory control systems, critical infrastructure, transport systems and many, many other things.In a sensor system, a sensor transforms a physical quantity into an analog signal which is sent to an ADC and a microcontroller for digitization and further processing. Once the measurement is in digital form, the microcontroller can execute tasks according to the measurement. Electromagnetic interference (EMI) can affect a measurement as it is transferred to the microcontroller. An attacker can manipulate the sensor output by intentionally inducing EMI in the wire between the sensor and the microcontroller. The nature of the analog channel between the sensor and the microcontroller means that the microcontroller cannot authenticate whether the measurement is from the sensor or the attacker. If the microcontroller includes incorrect measurements in its control decisions, it could have disastrous consequences.We present a novel detection system for these low-level electromagnetic interference attacks. Our system is based on the idea that if the sensor is turned off, the signal read by the microcontroller should be 0V (or some other known value). We use this idea to modulate the sensor output in a way that is unpredictable to the adversary. If the microcontroller detects fluctuations in the sensor output, the attacking signal can be detected. Our proposal works with a minimal amount of extra components and is thus cheap and easy to implement.We present the working mechanism of our detection method and prove the detection guarantee in the context of a strong attacker model. We implement our approach in order to detect adversarial EMI signals, both in a microphone system and a temperature sensor system, and we show that our detection mechanism is both effective and robust.

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