Machine Learning-Based Methodology for Preventing Ransomware Attacks on Healthcare Sector
Bibliographic record
Abstract
There is a big concern about security in health care organizations, to protect the important documents of patients, doctors, staff of the organizations and many more other information. In this current scenario where attacks have become common, proposing a new technology has become crucial concern. As Machine Learning is emerging in providing smart solutions in numerous applications and these techniques are also beneficial for network administrators to protect network infrastructure from multiple cyber-attacks at early stage. This research paper proposes a model to shield the hospital web server from directly receiving malicious packets. A dedicated machine learning trained server is suggested to be utilized in the health care network so that only authenticated data packet is transmitted inside the hospital network. Here in the paper Machine Learning approaches are used to identify malware, and among these techniques Random Forest proves to be the best algorithm for the early prediction of ransomware attacks. The dataset for training and testing machine learning model is taken from Canadian institute for Cybersecurity where data is pre-processed so, different validation code has been introduced like k-fold validation, confusion matrix, and Receiver Operating Characteristic Area Under the Curve to get a more rectify comparative analysis.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".