IoT Security Techniques Based on Machine Learning: How Do IoT Devices Use AI to Enhance Security?

By Liang Xiao, Xiaoyue Wan, Xiaozhen Lu, Yanyong Zhang, Di Wu. IEEE Signal Processing Magazine, 2018

628 citations recorded by the source corpus.

The Internet of things (IoT), which integrates a variety of devices into networks to provide advanced and intelligent services, has to protect user privacy and address attacks such as spoofing attacks, denial of service (DoS) attacks, jamming, and eavesdropping. We investigate the attack model for IoT systems and review the IoT security solutions based on machine-learning (ML) techniques including supervised learning, unsupervised learning, and reinforcement learning (RL). ML-based IoT authentication, access control, secure offloading, and malware detection schemes to protect data privacy are the focus of this article. We also discuss the challenges that need to be addressed to implement these ML-based security schemes in practical IoT systems.

Subfields: Computer Networks and Communications, Control and Systems Engineering, Signal Processing

View Paper: https://www.semanticscholar.org/paper/770990076b4dd6bd9078ec6fe3d9680bcf766dde

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

In Control and Systems Engineering

In Signal Processing

Citations in the corpus

Toward Massive Machine Type Communications in Ultra-Dense Cellular IoT Networks: Current Issues and Machine Learning-Assisted Solutions (2018, methodology): “Furthermore, IoT devices can utilize different unsupervised techniques including multivariate correlation analysis to detect DoS attacks and infinite Gaussian mixture model for physical layer authentication [28].”
Survey of intrusion detection systems: techniques, datasets and challenges (2019, methodology): “Several algorithms and techniques such as clustering, neural networks, association rules, decision trees, genetic algorithms, and nearest neighbour methods, have been applied for discovering the knowledge from intrusion datasets (Kshetri & Voas, 2017; Xiao et al, 2018).”
Machine Learning Based Solutions for Security of Internet of Things (IoT): A Survey (2020, background): “[8] reviewed different IoT attack models such as spoofing attacks, denial of service attacks, jamming, and eavesdropping and mentioned their possible security solutions based on IoT authentication, ac-”
Survey on Network Slicing for Internet of Things Realization in 5G Networks (2021, methodology): “ML techniques (i.e., supervised learning, unsupervised learning and reinforcement learning) have been extensively used in improving network security, including authentication, access control, malware detection and anti-jamming ofﬂoading [229].”
Deep Reinforcement Learning for Cyber Security (2019, methodology): “Among these ML applications, unsupervised and supervised learning methods have been used widely for intrusion detection [10]–[15], malware detection [16]–[19], cyber-physical attacks [20], [21], and data privacy protection [22].”
A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures (2019, methodology): “Spooﬁng: Attacks from spoofers can be avoided by using Q-learning [176], Dyna-Q [176], Support Vector Machines (SVM) [177], Deep Neural Network (DNN) model [178], incremental aggregated gradient (IAG) [46], and distributed FrankWolfe (dFW) [179] techniques.”
A Supervised Intrusion Detection System for Smart Home IoT Devices (2019, background): “4) Spoofing: Manipulates fake identities to compromise the effectiveness of the IoT device by forging a large number of identities to act as legal nodes [8].”
A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security (2018, methodology): “[23] briefly considered the ML methods for protecting data privacy and security in the IoT context.”