A Blockchain-Based Malware Detection Model for IoT Devices
Bibliographic record
Abstract
Malware is malicious software designed to cause destructive actions that damage information systems and networks.Malware infections have increased rapidly, and malware types have become more sophisticated, making malware detection more difficult.However, the IoT technology is vulnerable to malware attacks, since such devices have a permanent internet connection and no security.This makes it easier for the hackers to access them.These malware attacks are becoming go-to attacks on hackers.Thus, new malware detection techniques are required to address this challenge.Building a blockchain solution that allows IoT devices to download files from the Internet and verify whether they are malicious is an urgent need.The recent emergence of blockchain technology represents a solution because of its features, such as decentralization, persistence, and anonymity.Blockchain can be used instead of ordinary databases in signature-based malware detection solutions to provide decentralization and integrity.Another vital usage for blockchain networks, especially for Android devices, is that they can offer heuristic malware detection techniques for low-resource devices.Moreover, using blockchain technology overcomes some difficulties in malware detection and improves the detection ratio compared with strategies that do not utilize blockchain technology.This study examined different malware detection models based on blockchain technology.Furthermore, blockchain technology's effects on malware detection are elaborated on, particularly in an Android environment.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".