Robust and Lightweight Modeling of IoT Network Behaviors From Raw Traffic Packets

Abstract

Machine Learning (ML)-based techniques are increasingly used for network management tasks, such as intrusion detection, application identification, or asset management. Recent studies show that neural network-based traffic analysis can achieve performance comparable to human feature-engineered ML pipelines. However, neural networks provide this performance at a higher computational cost and complexity, due to high-throughput traffic conditions necessitating specialized hardware for real-time operations. This paper presents lightweight models for encoding characteristics of Internet-of-Things (IoT) network packets; 1) we present two strategies to encode packets (regardless of their size, encryption, and protocol) to integer vectors: a shallow lightweight neural network and compression. With a public dataset containing about 8 million packets emitted by 22 IoT device types, we show the encoded packets can form complete (up to 80%) and homogeneous (up to 89%) clusters; 2) we demonstrate the efficacy of our generated encodings in the downstream classification task and quantify their computing costs. We train three multi-class models to predict the IoT class given network packets and show our models can achieve the same levels of accuracy (94%) as deep neural network embeddings but with computing costs up to 10 times lower; 3) we examine how the amount of packet data (headers and payload) can affect the prediction quality. We demonstrate how the choice of Internet Protocol (IP) payloads strikes a balance between prediction accuracy (99%) and cost. Along with the cost-efficacy of models, this capability can result in rapid and accurate predictions, meeting the requirements of network operators.