Enhancing Intrusion Detection for IoT and Sensor Networks Through Semantic Analysis and Self-Supervised Embeddings

Area of Science:

• Cybersecurity
• Machine Learning
• Network Security

Background:

• Increasing complexity and sophistication of cyber threats necessitate advanced network and sensor security solutions.
• Traditional intrusion detection methods face challenges with high network traffic volume and evolving attack vectors.

Purpose of the Study:

• To propose a novel machine learning-driven Intrusion Detection System (IDS) for improved intrusion detection.
• To enhance threat identification by incorporating multidimensional data analysis, including geospatial and self-supervised semantic features.

Main Methods:

• Developed a machine learning-driven IDS incorporating geospatial context and self-supervised semantic features.
• Utilized ensemble learning methods for improved detection accuracy.
• Validated the system on a dataset of over 100,000 records from China Mobile.

Main Results:

• Achieved an impressive 98.5% accuracy rate in detecting intrusions.
• Demonstrated the effectiveness of the proposed system in identifying complex cyber threats.
• Highlighted the potential for real-world deployment and adaptation to evolving cyber threats.

Conclusions:

• The novel IDS offers a significant advancement in intelligent cybersecurity tools.
• The framework is extensible to Internet of Things (IoT) and wireless sensor networks (WSNs).
• The system provides lightweight yet semantically enriched solutions for resource-constrained environments.