IoT Anomaly Detection using Multivariate

Abstract

Devices associated with Internet of Things are typically constrained in their resources and do not have the computational power necessary to analyze their input and detect anomalies that occur. Smart devices or and environmental sensors that measure temperature, air quality, or seismic activity are all built for specific purposes with minimal resources and often do not have enough security in place to protect against infiltration or detect abnormal behavior. Additionally, because these devices and sensors are typically always connected and transmit constant data in near real-time, the high dimensionality of the raw readings are extremely computationally intensive to analyze. A possible solution to reduce the dimensionality of the data while also extracting the most significant features is to use multivariate analysis techniques such as Principal Component Analysis. PCA is a method of multivariate analysis meant to reduce the size of matrices while not only keeping the most significant variables but also learning the interactions between them. In this paper, we propose exploring anomaly detection in IoT using multivariate analysis techniques to reduce the dimensionality of sensor input to reduce the computational complexity of analysis and learning the most significant variables. While the normal conditions of sensor data are often readily available, the size of the data makes it difficult to precisely determine instances of targeted anomalies. In this study, PCA is used to analyze the available features of the data and from them can determine the sensors under normal conditions. Once the normal conditions are determined, outliers which constitute anomalies can be determined through techniques such as Mahalanobis distance to determine the variance of each observation from the normal distribution. Our work can also be expanded to use other methods of dimensionality reduction and feature extraction such as t-Distributed Stochastic Neighbor Embedding.