Optimized Machine Learning-Based Intrusion Detection System for Internet of Vehicles
Bibliographic record
Abstract
Internet of Vehicles (IoV) represents the application of Internet of Things (IoT) within vehicular communication environments. Internet of vehicles refers to a network of interconnected sensors, network layers, and communication systems that enable vehicles to connect with everything (V2X communication). Io V networks face numerous security challenges due to the emergence of modern types of attacks with unusual patterns. Therefore, it is a crucial and demanding task to design intelligent Intrusion Detection Systems (IDSs) for Io V networks. In this paper, we propose an optimized Machine Learning-based IDS to detect attacks in Io V networks. We deploy highly efficient Ma-chine Learning models, Light Gradient Boosting Machine, Extra Trees Classifier, and Extreme Gradient Boosting, to detect attacks in the CICDDoS2019 dataset. We apply the Synthetic Minority Oversampling Technique to resolve the issue of imbalanced data distribution of target class. A Correlation-based Feature Selection is conducted to reduce the computational cost by decreasing the number of input variables. In order to enhance the performance of the attack detection, hyperparameters are optimized using the Bayesian Optimization algorithm. The performance evaluation results show that these ML models perform well. Notably, the Extreme Gradient Boosting classifier outperforms other Machine Learning models, and our proposed solution outperforms existing systems in terms of Accuracy score.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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".