Machine Learning for the Detection and Identification of Internet of Things (IoT) Devices: A Survey

by   Yongxin Liu, et al.

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, non-cryptographic IoT device identification and rogue device detection become efficient solutions to secure existing systems and will provide additional protection to systems with cryptographic protocols. Non-cryptographic approaches require more effort and are not yet adequately investigated. In this paper, 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: device-specific pattern recognition, Deep Learning enabled device identification, unsupervised device identification, and 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, continual learning, and abnormality detection.



page 1


IoTSense: Behavioral Fingerprinting of IoT Devices

The Internet-of-Things (IoT) has brought in new challenges in, device id...

Position paper: A systematic framework for categorising IoT device fingerprinting mechanisms

The popularity of the Internet of Things (IoT) devices makes it increasi...

Class-Incremental Learning for Wireless Device Identification in IoT

Deep Learning (DL) has been utilized pervasively in the Internet of Thin...

Application of Machine Learning-Based Pattern Recognition in IoT Devices: Review

The Internet of things (IoT) is a rapidly advancing area of technology t...

RL-IoT: Reinforcement Learning to Interact with IoT Devices

Our life is getting filled by Internet of Things (IoT) devices. These de...

Performance Evaluation of Cryptographic Ciphers on IoT Devices

With the advent of Internet of Things (IoT) and the increasing use of ap...

U2Fi: A Provisioning Scheme of IoT Devices with Universal Cryptographic Tokens

Provisioning is the starting point of the whole life-cycle of IoT device...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.