Research Area:  Machine Learning
The Internet of Things (IoT) is becoming an indispensable part of everyday life, enabling a variety of emerging services and applications. However, the presence of rogue IoT devices has exposed the IoT to untold risks with severe consequences. The first step in securing the IoT is detecting rogue IoT devices and identifying legitimate ones. Conventional approaches use cryptographic mechanisms to authenticate and verify legitimate devices identities. However, cryptographic protocols are not available in many systems. Meanwhile, these methods are less effective when legitimate devices can be exploited or encryption keys are disclosed. Therefore, noncryptographic IoT-device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Noncryptographic approaches require more effort and are not yet adequately investigated. In this article, we provide a comprehensive survey on machine learning technologies for the identification of IoT devices along with the detection of compromised or falsified ones from the viewpoint of passive surveillance agents or network operators. We classify the IoT-device identification and detection into four categories: 1) device-specific pattern recognition; 2) deep learning-enabled device identification; 3) unsupervised device identification; and 4) abnormal device detection. Meanwhile, we discuss various ML-related enabling technologies for this purpose. These enabling technologies include learning algorithms, feature engineering on network traffic traces and wireless signals, incremental learning, and abnormality detection.
Author(s) Name:  Yongxin Liu; Jian Wang; Jianqiang Li; Shuteng Niu; Houbing Song
Journal name:  IEEE Internet of Things Journal
Publisher name:  IEEE
Volume Information:  Volume: 9, Issue: 1, Page(s): 298 - 320
Paper Link:   https://ieeexplore.ieee.org/document/9492269