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

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 ۱۲۸ memory units, optimized for deployment on edge servers and trained on the CIC-IDS۲۰۱۷ dataset. The model achieves outstanding performance, with a detection rate of ۹۹.۹۰%, accuracy of ۹۹.۹۰%, and an F۱ score of ۹۸.۹۳%. 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.

کلیدواژه ها:

anomaly detection ، Gaussian Noise ، Hurst Parameter ، intrusion detection system ، LSTM Network