A Modified Transformer Neural Network (MTNN) for Robust Intrusion Detection in IoT Networks
Bibliographic record
Abstract
The growth of the Internet of Things (IoT) in various industries has been unprecedented over the past few decades. However, IoT devices are prone to malicious network entities (i.e., attacks) such as data theft, phishing, spoofing, and denial of service attacks (DDoS attacks). These can result in additional cyber security risks, such as ransomware attacks and significant data breaches, which can cost firms a lot of money and time to repair. Thus, there is a great demand and need for building robust Intrusion detection systems (IDS) for real-time identification and recognition of these attacks. With the advances in neural networks, various models have been proposed. However, most traditional models lack the detection of diverse attack types due to their limited adaptability. Thus, in this paper, we propose a network Intrusion Detection System based on the attention mechanism of transformer neural networks, namely, the MTNN model. For performance evaluation, a traditional LSTM and RNN are also implemented and compared to the proposed model using the ToN_IoT dataset. Experimental results show that the MTNN model has achieved an improvement of up to 57%, 33%, 70%, and 63% for accuracy, precision, recall, and F-score, respectively.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".