Low Power Unsupervised Anomaly Detection by Nonparametric Modeling of Sensor Statistics

Abstract

This article presents anomaly detection by examining sensor stream statistics (AEGIS), a novel mixed-signal framework for real-time AEGIS. AEGIS utilizes kernel density estimation (KDE)-based nonparametric density estimation to generate a real-time statistical model of the sensor data stream. The likelihood estimate of the sensor data point can be obtained based on the generated statistical model to detect outliers. We present CMOS Gilbert Gaussian cell-based design to realize Gaussian kernels for KDE. For outlier detection, the decision boundary is defined in terms of kernel standard deviation ( $\sigma _{\mathrm{ Kernel}}$ ) and likelihood threshold ( $P_{\mathrm{ Thres}}$ ). We adopt a sliding window to update the detection model in real time. We use time-series data set provided from Yahoo to benchmark the performance of AEGIS. A ${f}$ 1-score higher than 0.87 is achieved by optimizing parameters such as length of the sliding window and decision thresholds which are programmable in AEGIS. Discussed architecture is designed using 45-nm technology node and our approach on average consumes ~75- $\mu \text{W}$ power at a sampling rate of 2 MHz while using ten recent inlier samples for density estimation.