A Survey on the Applications of Semi-supervised Learning to Cyber-security
Bibliographic record
Abstract
Machine Learning’s widespread application owes to its ability to develop accurate and scalable models. In cyber-security, where labeled data is scarce, Semi-Supervised Learning (SSL) emerges as a potential solution. SSL excels at tasks challenging traditional supervised and unsupervised algorithms by leveraging limited labeled data alongside abundant unlabeled data. This article presents a comprehensive survey of SSL in cyber-security, focusing on countering diverse cybercrimes, particularly intrusion detection. Despite its potential, a notable research gap persists, with few recent studies comprehensively reviewing SSL’s application in cyber-security. This study examines state-of-the-art SSL techniques tailored for cyber-security to address this gap. Relevant methods are identified, and their effectiveness is evaluated to empower researchers and practitioners with insights to enhance cyber-security measures. This work sheds light on SSL’s potential in addressing data scarcity in cyber-security domains in addition to outlining new research directions to advance this crucial field. By bridging this research gap, this manuscript paves the way for enhanced cyber-threat detection and mitigation in an increasingly interconnected world.
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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".