A Description Logic Ontology for Email Phishing
Bibliographic record
Abstract
Phishing detection is an area of identifying malicious activities designed by phishers to lure users providing sensitive information. Existing anti-phishing systems use blacklists based on specific parameters, characterize attacker’s activities with artificial and computational approaches and educate users. The development and maintenance of these systems is hard and costly because of the polymorphic nature of phishing techniques. Phishing attacks are able to scam humans with insufficient knowledge, while countermeasures focus on specific characteristics to make decisions. Defining formal approaches for representing and reasoning knowledge in anti-phishing systems is therefore a concern. This work deals with this issue by proposing formalized description logic to build the knowledge base of phishing attacks. It additionally designs an ontology-oriented approach to add semantics on that knowledge. The ontology model has been proven consistent and satisfiable. Experimentations on case studies demonstrate the ability of the proposed model to represent knowledge attack scenarios. A comparison with state-of-the-art researches shows that the proposed formalism is more adequate to characterize phishing semantics. This work could successfully complement anti-phishing systems.
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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 | 0.000 | 0.000 |
| 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 | 0.001 |
| 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".