Tampering Attack Detection in Analog to Feature Converter for Wearable Biosensor

Wearable biosensors have been widely used to assist disease diagnosis or monitor health conditions, making the authorization to communicate with these biosensors very critical. The potential tampering attack may cause disasters that threaten human lives. In this paper, a tampering attack detection method is proposed for securing key parameters of a real-time ECG monitoring system. The detection method is based on a built-in triangle waveform and the corresponding extracted abnormal pattern vector examination. When the deviation of the pattern vector is above the defined attack detection threshold value, we could recognize that an attack occurs. Two representative records of ECG data are used to evaluate the different attack levels impact. The proposed tampering attack detection framework is implemented using 0.18 $\mu m$ standard CMOS process and costs 41413 $\mu m ^{2}$ chip area, with an estimated dynamic power consumption of 15 nW, which is very hardware-efficient and easy to be implemented.