An Ontology-Based Approach for Zero-Day Information Security Threat Management
Bibliographic record
Abstract
Zero Day security threats are diverse and manifest in many forms. Despite the growing number of zero day attacks, very little information about the kind of threat and how to defend against the threats is known by information security professionals. Signature based techniques and statistical based techniques have been seen to be less effective in handling Zero-day security threats (ZDST) since they require a new threat signature and threat profile to be learnt each time, meaning new signatures and profiles cannot be detected and behavior-based approaches have always resulted in many false positives in handling of zero-day security threats. The ZDST may result in disruptions of service, loss of data, loss of data integrity, corruption of data, systems malfunction, miscommunication, or other undesired effects on information systems. This research proposes an ontology-based approach for management of ZDST and evaluates its performance for use in detection and prevention of ZDST within the information security domain.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.009 |
| Open science | 0.001 | 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".