Machine Learning and Deep Learning Techniques for Internet of Things Network Anomaly Detection—Current Research Trends

With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of a...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 24; no. 6; p. 1968
Main Authors: Rafique, Saida Hafsa, Abdallah, Amira, Musa, Nura Shifa, Murugan, Thangavel
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 20.03.2024
MDPI
Subjects:
Algorithms
anomaly
Artificial intelligence
Datasets
Deep learning
Detectors
Forecasts and trends
Internet of Things
intrusion detection
Intrusion detection systems
Machine learning
Methods
Middleware
Neural networks
Review
Smart houses
Software
deep learning
intrusion detection
anomaly
Internet of Things
machine learning
artificial intelligence
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:With its exponential growth, the Internet of Things (IoT) has produced unprecedented levels of connectivity and data. Anomaly detection is a security feature that identifies instances in which system behavior deviates from the expected norm, facilitating the prompt identification and resolution of anomalies. When AI and the IoT are combined, anomaly detection becomes more effective, enhancing the reliability, efficacy, and integrity of IoT systems. AI-based anomaly detection systems are capable of identifying a wide range of threats in IoT environments, including brute force, buffer overflow, injection, replay attacks, DDoS assault, SQL injection, and back-door exploits. Intelligent Intrusion Detection Systems (IDSs) are imperative in IoT devices, which help detect anomalies or intrusions in a network, as the IoT is increasingly employed in several industries but possesses a large attack surface which presents more entry points for attackers. This study reviews the literature on anomaly detection in IoT infrastructure using machine learning and deep learning. This paper discusses the challenges in detecting intrusions and anomalies in IoT systems, highlighting the increasing number of attacks. It reviews recent work on machine learning and deep-learning anomaly detection schemes for IoT networks, summarizing the available literature. From this survey, it is concluded that further development of current systems is needed by using varied datasets, real-time testing, and making the systems scalable.
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ISSN:1424-8220
1424-8220
DOI:10.3390/s24061968