WiP: The Intrinsic Dimensionality of IoT Networks

Abstract

The Internet of Things (IoT) is revolutionizing society by connecting people and devices seamlessly and providing enhanced user experience and functionalities. However, the unique properties of IoT networks, such as heterogeneity and non-standardized protocol, have created critical security holes and network mismanagement. We propose a new measurement tool for IoT network data to aid in analyzing and classifying such network traffic. We use evidence from both security and machine learning research, which suggests that the complexity of a dataset can be used as a metric to determine the trustworthiness of data. We test the complexity of IoT networks using Intrinsic Dimensionality (ID), a theoretical complexity measurement based on the observation that a few variables can often describe high dimensional datasets. We use ID to evaluate four modern IoT network datasets empirically, showing that, for network and device-level data generated using IoT methodologies, the ID of the data fits into a low dimensional representation; this makes such data amenable to the use of machine learning algorithms for anomaly detection.