Cyber Security Challenges in Autonomous Vehicle: Their Impact on RF Sensor and Wireless Technologies
Bibliographic record
Abstract
Connected and Autonomous Vehicles (CAVs) will play a crucial role in nextgeneration and intelligence cars. Autonomous Vehicles (AV) are equipped with the advanced RF and wireless communication and sensor infrastructure for supporting vehicular network. AVs not only help to empower aging populations, but also they provide a powerful, noninvasive, non-threatening modality by which to monitor, interact with, and respond to their passengers' state of being. Ten million AVs or self-driving cars will be on the road by 2020, according to an indepth report recently. Proponents of AVs say that the technology has the potential to benefit society in a range of ways, from boosting economic productivity to reducing urban congestion. However, others including some potential consumers and corporate risk managers have expressed serious concerns over the cybersecurity of the so-called fleet of the future. In this research, we will describe how to develop and implement such functionalities highly secure, trust, reliable and accurate sensors. RF sensors like radars and multitude of other types of RF sensors arrays (IoT based) are an instrumental part of the general sensor configuration required for implementing a robust cyber-security strategy and enhancing privacy in addition to the other autonomous features. There are some challenges and threats such as: : What is a new and important threat in AV and what is the difference between it and the other threat occurred in cyber space? : How can AV be attacked? : What is the difference between security and privacy mechanisms for AV? : Could the risk management be ridable to define, control, and mitigate the risk in an AV? : Could cyber-criminals remotely hijack an autonomous car's electronics with the intention of causing a crash? : Could terrorists command the vehicles similar to the weapons? : Could data stored on-board be unlocked? : Could the AV privacy and passengers be considered? : Is there any secure channel in V2V connection for AV?
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".