Integrating CNN-LSTM Networks with Statistical Filtering Techniques for Intelligent IoT Intrusion Detection
Fatemeh Imani, Masoud Kargar, Alireza Assadzadeh, Ali Bayani · 2024
In the rapidly evolving landscape of the Internet of Things (IoT), securing networked devices against malicious intrusions is of paramount importance. This paper presents a novel approach for Intelligent IoT Intrusion Detection, leveraging integrated Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) networks, along with Statistical Filtering techniques for denoising. By synergistically combining deep learning architectures with statistical filtering, our methodology aims to enhance the accuracy and robustness of intrusion detection systems in IoT environments. Empirical evaluations on diverse datasets, including the Edge-IIoTset Dataset and CoAP-DoS Dataset, demonstrate the effectiveness of our approach, achieving competitive accuracy rates of 99.93% and 94.99% respectively. Furthermore, our model showcases promising computational efficiency, making it suitable for deployment on resource-constrained IoT devices. These results underscore the significance of our research in advancing the state-of-the-art in IoT privacy, paving the way for more resilient and adaptive intrusion detection systems.