Ransomware: A Framework for Security Challenges in Internet of Things
Bibliographic record
Abstract
With the increasing volume of smartphones, computers, and sensors in the Internet of Things (IoT) model, enhancing security and preventing ransom attacks have become a major concern. Traditional security mechanisms are no longer applicable due to the involvement of devices with limited resources, which require more computing power and resources. Ransomware is comparatively a new and cruel malware in cyberspace with higher rates of attacks around the world. Ransomware could encrypt entire data to make users unable to access their files and important information. In some cases, the system has been hostage completely by the hackers, and the user may receive a demand for ransom money using different resources o access of his/her own data/system. One of the problems associated with the Internet of Things is how to keep your smartphones secure and keep your data safe as most of the antivirus solutions are not useful in this case. This research concludes the impact of ransomware on the IoT, malware processes, and work on detecting and monitoring smartphone infections. The paper also discusses ransomware awareness to end-user with strategy to defeat it.
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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".