IoT Behavioral Monitoring via Network Traffic Analysis

• URL: http://arxiv.org/abs/2001.10632v1
• Date: Tue, 28 Jan 2020 23:13:12 GMT
• Title: IoT Behavioral Monitoring via Network Traffic Analysis
• Authors: Arunan Sivanathan
• Abstract summary: This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs. We develop a robust machine learning-based inference engine trained with attributes from traffic patterns. We demonstrate real-time classification of 28 IoT devices with over 99% accuracy.
• Score: 0.45687771576879593
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Smart homes, enterprises, and cities are increasingly being equipped with a plethora of Internet of Things (IoT), ranging from smart-lights to security cameras. While IoT networks have the potential to benefit our lives, they create privacy and security challenges not seen with traditional IT networks. Due to the lack of visibility, operators of such smart environments are not often aware of their IoT assets, let alone whether each IoT device is functioning properly safe from cyber-attacks. This thesis is the culmination of our efforts to develop techniques to profile the network behavioral pattern of IoTs, automate IoT classification, deduce their operating context, and detect anomalous behavior indicative of cyber-attacks. We begin this thesis by surveying IoT ecosystem, while reviewing current approaches to vulnerability assessments, intrusion detection, and behavioral monitoring. For our first contribution, we collect traffic traces and characterize the network behavior of IoT devices via attributes from traffic patterns. We develop a robust machine learning-based inference engine trained with these attributes and demonstrate real-time classification of 28 IoT devices with over 99% accuracy. Our second contribution enhances the classification by reducing the cost of attribute extraction while also identifying IoT device states. Prototype implementation and evaluation demonstrate the ability of our supervised machine learning method to detect behavioral changes for five IoT devices. Our third and final contribution develops a modularized unsupervised inference engine that dynamically accommodates the addition of new IoT devices and/or updates to existing ones, without requiring system-wide retraining of the model. We demonstrate via experiments that our model can automatically detect attacks and firmware changes in ten IoT devices with over 94% accuracy.

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