ACNN-IDS: An Attention-Based CNN for Cyberattack Detection in IoT
Zil e Huma, Jawad Elsayed Ahmad, Hussam Al Hamadi, Baraq Ghaleb, William Johnston Buchanan, Sana Ullah Jan · 2024
The Internet of Things (IoT) has become an integral part of modern societies, with devices, networks, and applications offering industrial, economic, and social benefits. However, these devices and networks generate vast amounts of data, making them a favourite target for cybercriminals. This article introduces an attention-based convolutional neural network (ACNN) for cyberattack detection in IoT. The proposed ACNN incorporates an Inception architecture with a spatial attention mechanism. This integration optimizes feature extraction and localization, bolstering the IDS's accuracy and efficiency. The efficacy of the designed IDS is investigated through several parameters using the CIC-IDS 2018 dataset. The experimental outcomes indicate that the proposed ACNN achieved a higher attack detection accuracy of 98.11% and successfully classified 14 classes. The model size was determined as 21.172 MB which can be easily deployed on resource-constrained IoT devices. The model exhibited a computational performance of 5.804 GFLOPS when executed on an NVIDIA T4 GPU, and its inference time was recorded as 0.1867 ms.