IoT Anomaly Detection using Multivariate

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 in