Intrusion detection system using Optimized Machine Learning Algorithms for cyberattacks in the Internet of Vehicles (IoV)
Bibliographic record
Abstract
The Internet of Vehicles (IoV) is a branch of the Internet of Things that deals with vehicle-to-vehicle communication and intelligent transport systems (ITS). But this connection is not without consequences, because the more a system exchanges information, the more vulnerable it is to various attacks from malicious actors. (hackers). Vehicle Internet security is a great challenge that security professionals face every day. Moreover, despite the deployment of diverse technologies by smart cities to obtain varied, high-performance cloud services, security concerns continue to appear in communications entities that share information. In this article, an intrusion detection system (IDS) based on machine learning is proposed to improve safety in vehicle Internet systems (IoV). The IDS uses random forest (RF) algorithms, decision Tree, Adaboost, and gradient boost on an IoV traffic data set. The hyperparameters of machine learning models are optimized using a meta-heuristic optimization algorithm called the CDO (Chemotactic Differential Evolution) algorithm. The proposed IDS achieved high performance in terms of up to 99.91% accuracy for the Adaboost algorithm in the binary case and 9.81% accuracy in the case of the decimal dataset. High performance of precision, recall, and F1 score were also observed in this study. The optimization has significantly improved the performance of the models by optimizing their hyperparameters. The study was conducted using data sets built by CICIS (Canadian Institute of Cybersecurity) with a real vehicle to evaluate the proposed detection system. The experimental results show that the proposed IDS has significantly higher detection performance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".