A Novel Intrusion Detection System for RPL-Based Cyber–Physical Systems
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
The physical layer of cyber-physical systems (CPSs) is composed of resource-constrained devices connected in a wireless sensor network (WSN). Although this layer is easy to deploy, in most cases, it has many security issues. Several intrusion detection systems (IDSs) have been proposed and tested as effective and efficient solutions to detect only a few known attacks. In this article, we propose a novel, Supervised machine learning-based IDS that is capable of detecting several attacks. This article discusses all IDS design steps, starting from data collection to the feature engineering analysis and building the trained models. Experimental results show that the proposed IDS can detect four different types of attacks that were seen by the machine learning models during the training phase. The IDS can also detect the existence of several other attacks that are not seen by the model and classify them as unknown attack types. The proposed model achieves 99.97% classification accuracy when detecting known attacks and 85% classification accuracy when detecting a new attack type.
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 | 0.000 |
| Science and technology studies | 0.000 | 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