An Ensemble-Based Hybrid Model for the Detection of Attacks in the Internet of Vehicular Things
Bibliographic record
Abstract
The Internet of Vehicles (IoV) enables technology that allows IoV and vehicles to connect everything. IoV has become an essential component of modern life. This exponential growth of IoV technology has introduced significant security and privacy issues, which pose potential threats to different types of attacks and cause different threats to the normal operation of vehicles. To prevent intelligent vehicle accidents and identify malicious attacks within IoV networks, various researchers have focused on machine learning (ML)-based methods to detect attacks. Intrusion detection systems (IDS) are a prominent solution for cyber attacks in IoV using ensemble learning. To achieve higher accuracy and detection rate, designing an improved detection framework using ensemble learning is a challenging task. The design of an ensemble-based IDS depends on two main challenges: selecting base classifiers and their combination methods. Therefore, in this study, we propose a hybrid ML model to detect various attacks in IoV. We have used different ML algorithms to develop an enhanced algorithm that can efficiently detect attacks in IoV networks. To evaluate the performance of the proposed system, we have used two well-known datasets, (CIC-IDS2017) and (UNSW-NB15). The proposed algorithm shows outstanding performance from the performance results, with an average attack detection accuracy of 99.75% and 100% and an F1 score of 99.74% and 100%, respectively, for both datasets. Further performance scores, that is, recall, precision, and F1 score metrics, validate the exceptional effectiveness of the proposed framework.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".