Intrusion detection in IoT and wireless networks using image-based neural network classification

Telecommunication networks play more and more important role in our modern times, and there are significant security risks associated with both wireless and wired networks. These risks stem from various malicious actions and security threats that have emerged with the development of Fourth Generation (4G), Fifth Generation (5G), and Internet of Things (IoT) networks. Machine learning (ML) algorithms have been applied to Intrusion Detection Systems (IDSs) due to their capacity to their ability to detect complex network traffic patterns. Deep learning (DL) networks are highly effective in processing images and videos and they have potential to solve other types of data. Given the characteristics of network traffic records used for intrusion detection in wireless and wired networks, we propose a simple data preprocessing method to convert the data into a grid-structured format, making it compatible with image processing networks. To validate the proposed structure, modified LeNet networks have been used for intrusion detection based on the NSL-KDD and CICIoV2024 (Canadian Institute for Cybersecurity Internet of Vehicles 2024 dataset) benchmark datasets. The simulation results indicate that methods based on extracted features may not always guarantee improved performance. The proposed Image Classification Neural Network-based Intrusion Detection (ICNN-ID) outperforms the compared existing methods. The multiclass classification experimental results show that the proposed LeNet-based IDS achieved a test accuracy (TAC) of 89.97% for NSL-KDD and nearly 100% (99.996%) for CICIoV2024. Additionally, it offers higher accuracy and improved robustness compared to a one-dimensional CNN and a recent deep learning model that integrates deep convolutional neural networks (DCNN) and bidirectional long short-term memory (BiLSTM). • Proposed ICNN-ID: a ICNN-based framework for intrusion detection. • Introduced a forced minority inclusion method to address class imbalance. • Developed a fixed-random conversion technique for grid-based ICNN input. • Validated model performance using NSL-KDD and CICIoV2024 datasets. • Compared ICNN-ID with recent models using F1, Precision, Recall, and ROC curves.