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

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.