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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

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

Saida Hafsa Rafique¹, Amira Abdallah¹, Nura Shifa Musa^1,2

¹College of Information Technology, United Arab Emirates University, Abu Dhabi P.O. Box 15551, United Arab Emirates.

Sensors (Basel, Switzerland)

|March 28, 2024

Summary

Anomaly detection in the Internet of Things (IoT) is enhanced by Artificial Intelligence (AI). This review explores machine learning and deep learning for detecting threats in IoT networks, highlighting the need for improved systems.

Keywords:

Internet of Things anomaly artificial intelligence deep learning intrusion detection machine learning

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Area of Science:

Computer Science
Cybersecurity
Artificial Intelligence

Background:

The Internet of Things (IoT) is rapidly expanding, generating vast amounts of data and increasing connectivity.
Anomaly detection is crucial for identifying deviations from normal system behavior in IoT environments.
The integration of Artificial Intelligence (AI) significantly improves the effectiveness of anomaly detection in IoT systems.

Purpose of the Study:

To review the existing literature on anomaly detection in IoT infrastructure.
To explore the application of machine learning and deep learning techniques for anomaly detection in IoT.
To identify challenges and future research directions in IoT anomaly detection.

Main Methods:

Literature review of anomaly detection schemes in IoT networks.
Analysis of machine learning and deep learning approaches for intrusion detection.
Summary of recent advancements and challenges in the field.

Main Results:

AI-powered anomaly detection effectively identifies various threats in IoT, such as DDoS attacks and SQL injection.
Intelligent Intrusion Detection Systems (IDSs) are vital for securing IoT devices with large attack surfaces.
The study categorizes and reviews numerous machine learning and deep learning-based anomaly detection methods.

Conclusions:

Further development is required to enhance current anomaly detection systems for IoT.
Recommendations include utilizing diverse datasets, conducting real-time testing, and ensuring system scalability.
Continued research is essential to address the evolving landscape of cyber threats in IoT.