Intrusion detection in IoT and wireless networks using image-based neural network classification
Bibliographic record
Abstract
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).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".