Analysis of lightweight CNN-Based Intrusion Detection Models in IoT

• Published in: 2024 IEEE/ACM International Conference on Big Data Computing, Applications and Technologies (BDCAT) pp. 314 - 323
• Main Authors: Abdel-Ghani, Muraam, Zakraoui, Jezia, Belhi, Abdelhak, Al-Ali, Abdulaziz, Rahme, Sandy, Bouras, Abdelaziz
• Format: Conference Proceeding
• Language: English
• Published: IEEE 16.12.2024
• Subjects: 1D raw data; 2D raw data; CNN-based Network Intrusion detection; Computational modeling; Cyberattack; Data models; Hands; Internet of Things; IoT; lightweight CNN; Memory management; Network intrusion detection; Performance evaluation; Power demand; Telecommunication traffic; web attack classification
• DOI: 10.1109/BDCAT63179.2024.00056

The Internet of Things (IoT) has become an integral part of our daily lives. While modern interactions have become more convenient due to the myriad of IoT devices, this diversity also makes IoT devices fertile targets for cyber attacks. However, due to the resource constraints of IoT deployment devices, intrusion detection schemes must be customized to meet the specific requirements of the IoT environment, especially in terms of power consumption and computing performance. In this paper, we benchmark multiple lightweight CNN-based models using public IoT network traffic datasets due to their wide popularity in network traffic classification. We evaluated also 1D and 2D variants of an optimized CNN model. Empirical results reveal that 1D models tend to perform better than 2D variants and other evaluated popular lightweight models. On the other hand, 2D-CNN offers less computation time and less memory footprint when compared with 1D-CNN indicating better efficiency. We further subject the competing methods to an early intrusion detection experiment. Results indicate that intrusions are successfully detected using as few as 6 initial packets of a session.