Simulating Attacks for RPL and Generating Multi-class Dataset for Supervised Machine Learning
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
Routing protocol for low power and lossy network (RPL) is one of the most common routing protocols used at the physical layer of the Cyber Physical Systems (CPS). This paper focuses on analyzing the security threats in RPL and the possible attacks that could affect the CPS network. The paper presents a new framework to simulate RPL attacks using contiki-Cooja. We have simulated four different attacks using this framework. Also, through the experimental work, this paper analyzes the features extracted from the network traffic packets and proposes a new machine learning model. Using several feature reduction techniques, the number of features required for the classification of the attacks are reduced from 58 to 21 i.e. 63.7% reduction to save processing and communication energy.The dataset generated using the feature engineering is used to develop a machine learning model that can detect those four different attacks on the CPS network. Our experimental results show that we can achieve a classification accuracy of 99.33% using RandomForest classifier.
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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