Anomaly Detection in IoT Traffic in the Presence of Gaussian Noise Using Deep Neural Networks

Morshedi, Roya; Matinkhah, S. Mojtaba

doi:10.22044/jadm.2025.15672.2684

Articles in Press

Document Type : Technical Paper

Authors

Department of Computer Engineering, Yazd University, Yazd, Iran.

10.22044/jadm.2025.15672.2684

Abstract

The Internet of Things (IoT) is a rapidly growing domain essential for modern smart services. However, resource limitations in IoT nodes create significant security vulnerabilities, making them prone to cyberattacks. Deep learning models have emerged as effective tools for detecting anomalies in IoT traffic, yet Gaussian noise remains a major challenge, impacting detection accuracy. This study proposes an intrusion detection system based on a simple LSTM architecture with 128 memory units, optimized for deployment on edge servers and trained on the CIC-IDS2017 dataset. The model achieves outstanding performance, with a detection rate of 99.90%, accuracy of 99.90%, and an F1 score of 98.93%. A key innovation is integrating the Hurst parameter with the model, improving resilience against Gaussian noise and enhancing detection of attacks like DoS and DDoS. This research highlights the value of advanced statistical features and robust noise-resistant models in securing IoT networks. The system’s precision, rapid response, and innovative approach mark a significant advance in IoT cybersecurity.

Keywords

Main Subjects

B.3. Communication/Networking and Information Technology

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Journal of AI and Data Mining

Anomaly Detection in IoT Traffic in the Presence of Gaussian Noise Using Deep Neural Networks

References

References

Articles in Press, Accepted Manuscript
Available Online from 06 May 2025

Anomaly Detection in IoT Traffic in the Presence of Gaussian Noise Using Deep Neural Networks

References

References

Articles in Press, Accepted Manuscript Available Online from 06 May 2025

Articles in Press, Accepted Manuscript
Available Online from 06 May 2025