A Systematic Literature Review on Internet of Vehicles Security Challenges and Penetration Testing Solutions
Bibliographic record
Abstract
This systematic literature review examines security challenges and penetration testing mitigations within the Internet of Vehicles (IoV), integrated with 5G technology. While IoV improves traffic control, road safety and sustainable transportation, it incurs serious cybersecurity vulnerabilities. Cyber threats include Denial of Service (DoS), Man in the Middle (MitM), and eavesdropping are very common in this paradigm. They can eventually compromise the integrity, confidentiality, and availability of vehicular data, leading to traffic accidents and data leakage. According to Kitchenham guidelines, this review follows an extensive search about peer-reviewed articles in the past 5 years. This synthesizes the findings to reveal critical vulnerabilities in the IoV systems, more particularly those it exacerbates by 5G technology, and underscores the importance of penetration testing. It is an important way of identifying and fixing the system vulnerabilities through a simulated cyberattack within a controlled environment before such vulnerabilities may be used by malevolent actors. Effective strategies are also reviewed, such as compliance testing, machine learning for anomaly detection, and advanced model-checking tools such as Scyther and Tamarin. The review also explores current penetration testing practices updated to address dynamic cyber threats. Case studies, such as security and privacy issues of 5G-enabled autonomous platoons, demonstrate practical applications. Suggestions for future research include enhancing threat detection and prediction techniques, developing security frameworks tailored to IoV environments, and fostering cooperation between academia and industry. This SLR is a valuable resource for researchers and practitioners interested in securing IoV systems against evolving cyber threats, particularly as these systems advance with 5G technology.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".