Towards an Explainable Universal Feature Set for IoT Intrusion Detection
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
As IoT devices' adoption grows rapidly, security plays an important role in our daily lives. As part of the effort to counter these security threats in recent years, many IoT intrusion detection datasets were presented, such as TON_IoT, BoT-IoT, and Aposemat IoT-23. These datasets were used to build many machine learning-based IoT intrusion detection models. In this research, we present an explainable and efficient method for selecting the most effective universal features from IoT intrusion detection datasets that can help in producing highly-accurate and efficient machine learning-based intrusion detection systems. The proposed method was applied to TON_IoT, Aposemat IoT-23, and IoT-ID datasets and resulted in the selection of six universal network-flow features. The proposed method was tested and produced a high accuracy of 99.62% with a prediction time reduced by up to 70%. To provide better insight into the operation of the classifier, a Shapley additive explanation was used to explain the selected features and to prove the alignment of the explanation with current attack techniques.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it