sSecure Net: A Hybrid CNN-LSTM-based Intrusion Detection System for Securing IoT Networks
Ovi Abul Hussain, Zigang Chen, Haihua Zhu · 2025
The widespread deployment of IoT devices has seen the emergence of a huge security threat, and IoT networks are the favorite targets for numerous cyberattacks. Due to the scarce resources in most IoT devices, the conventional intrusion detection systems (IDS) are unable to catch up with the enormous traffic in IoT networks. This paper suggests a new intrusion detection model based on CNN-LSTM, incorporating deep learning and statistical filtering to increase the intrusion detection in IoT networks. The model uses Convolutional Neural Networks (CNN) to capture the spatial relationships and Long Short-Term Memory (LSTM) networks to capture the temporal relationships in IoT network traffic. The model also uses statistical filtering techniques, including Median Filtering and Standard Deviation-based Filtering, to pre-process the traffic and eliminate the noise and the outliers, making the model's precision improved. The model is tested based on the Edge-IIoTset dataset, comprised of different diverse attack types, including Denial of Service (DoS), SQL injection, Ransomware, and Man-in-the-Middle (MITM). The model gives 94.99% precision, reflecting the model's precision in detecting different intrusions. This paper sets the efficacy in the use of the integration between the use of CNN-LSTM and statistical filtering to build an efficient, scalable, and precise intrusion detection model in IoT networks. The findings offer a real-time solution to increase IoT security, in particular, intrusion detection in IoT networks in real-time.