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MEMS on springs to detect changes in

MEMS is synonymous with micro-electromechanical system and applies to anysensor produced using microelectronic manufacturing techniques. These techniques create mechanical sensing structures of microscopic dimensions.Capacitive MEMS accelerometer sensors use a sensing mass on springs to detect changes in speed or direction. These sensors measure the acceleration andmovement of objects in three dimensions. They can indicate how the user holds,or tilts his smartphone or tablet, and allow him to track his walking steps madein a day, for example. But these sensors are not only used by consumers, they arealso integrated into the circuits of certain medical devices, automotive vehicles,drones and even satellites.

Thousands of everyday devices already contain thesetiny MEMS accelerometers.A classical capacitive MEMS accelerometer sensor architecture is presented in(Figure 1). Under the influence of acceleration forces, the sensor mass shifts andcauses a capacitance change denoted by DCapacitance, which is converted into ananalog voltage signal, denoted by s(t). The analog signal go through a low passfilter (LPF) to reduce undesirable frequencies , and it is amplified than convertedinto a digital signal using an analog-digital-converter (ADC) for digital processing.

3 Sonic Attacks on MEMS accelerometer 53.2 Modeling the sonic attackFigure 2: Delivering acoustic signals in close proximity 1.Figure 2 shows how sonic attacks on capacitive MEMS accelerometers are ableto be done by placing a speaker in close proximity to the accelerometer, and playing sounds at frequencies ranging from 2 to 30kHz, which have been proved to displace the sensing mass.

Since these accelerometers use the displacement of thesensing mass for measuring acceleration, an electrical signal is generated. Thiselectrical signal generated by sound waves represents the acoustic acceleration,denoted by sa(t). In order to achieve a maximum acoustic disturbance, sa(t) canbe multiplied by an attenuation coefficient, A1 ? 1, which is a function of acoustic frequencies. By combining the acoustic acceleration with the true accelerations(t), the measured acceleration can be obtained, which is:s ˆ(t) = s(t) + A1 · sa(t) (1)The acoustic acceleration sa(t) is modeled as:sa(t) = A0 · cos(2pFat + f) (2)Where Fa is the acoustic frequency played at amplitude A0 and phase f. As aresult, the measured acceleration is:s ˆ(t) = s(t) + A0 · A1 · cos(2pFat + f) (3)Evaluation: To avoid external noises, researchers 1 evaluated the model insidean acoustic isolation chamber by placing an analog MEMS accelerometer over avibration platform vibrating at frequency 70 Hz. Moreover, a speaker was placedabove the sensor at a distance of 10 cm to decouple the sensor from mechanicalvibrations.

The sensor’s output was sampled by an ADC at 7 kHz and logged by acomputer. Fa was modulated on and off at 0.5 Hz.Results: Researchers 1 concluded, that the measured acceleration performedthe combination of the true acceleration and the acoustic acceleration, which confirms the accuracy of the model.

6 3.4 Controlling the accelerometer’s output3.3 Sonic attack requirementBased on the model represented in the subsection 3.2, an attacker could spoofthe output of an accelerometer easily. Before the signals of the MEMS sensorcan be processed by a micro-controller, they need to be amplified and sampled toavoid aliasing prior to the analog-to-digital conversion by a low-pass filter (LPF).Trippel 1 classified three vulnerable areas as root elements allowing the attackerto attack successfully: less-than-accurate ADC, amplifier and LPF. For many accelerometers, these areas are already integrated on the component.

Furthermore,researchers 1 were able to deceive 15 of 20 different accelerometer models fromfive different manufacturers, and control the output of 65% of these devices.3.4 Controlling the accelerometer’s outputFigure 3: Output signal “WALNUT”.In his investigation, Trippel 1 examined 2 of 20 different models of MEMS andrealize that he can control the output either for a few seconds or indefinitely byusing signal aliasing or signal modulation.

His research team was able to spoof aspecific MEMS accelerometer to output a signal similar to the word “WALNUT”(Figure 3) using an Oscilloscope.the researchers 1 found that the output of such acclerometers can be attackedin two different ways:1. Output control attack “the attack can be done on the accelerometer that exhibit constant shifted false measurements” 1 at their resonant frequenciesdue to insecure amplifiers. In this attack, there is no need for signal aliasingat the ADC. By using amplitude modulation, it is possible to generate anysignal form at the accelerometer’s output. This attack gives an indefinite fullcontrol of the output.2. Output biasing attack In this attack, the sound frequency is chosen in sucha way that when the signal is being measured by the analog-to-digital converter (ADC), aliasing occurs.

By a modulation of the injected acoustic signal, an attacker can then generate the desired output signal. In this acousticinjection attack vector, the control of the output only succeeded for a fewseconds.

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