Security Vulnerabilities and Threats in Robotic Systems: A Comprehensive Review
Bibliographic record
Abstract
The recent digital revolution has resulted in robots being integrated more than ever into various domains, such as agriculture, healthcare, and the military.Robots are dedicated to serving, facilitating, and improving human life.However, the growing prevalence of robotics has brought to light the need for robust security measures.While unintentional accidents are inevitable, this paper focuses on the increasingly challenging problem of malicious cyber-attacks against robotic systems.One specific incident highlighting the severity of this issue occurred in 2022 when a healthcare robot was hacked, resulting in the misadministration of medication to several patients, leading to severe health complications and unnecessary loss of human lives.Such instances underscore the urgency of understanding the robotics domain's security vulnerabilities, threats, and consequences.In conclusion, this paper highlights the critical aspects of securing robotic systems in today's technologically advanced world.By identifying and analyzing the primary security vulnerabilities, this paper examines the primary security vulnerabilities, the type of application, then the impact of vulnerabilities; we can pave the way for effective security measures and ultimately ensure the safety and reliability of robotic systems.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".