Integrating Auxiliary Knowledge into Machine Learning to Improve the Detection of Cyberattacks
Bibliographic record
Abstract
Malicious activities are becoming more complex and difficult to detect, leading to a need for advanced solutions. Machine Learning (ML) presents several success cases across multiple industries and in cybersecurity, ML has demonstrated promising performance in the detection and classification of malicious activities. However, there are still critical limitations that prevent their wide adoption in cybersecurity operations (e.g., lack of interpretability and too many false positives). KnowledgeInfused Learning (KIL) has the potential to address current limitations through different techniques. One possible approach relies on the adoption of Auxiliary Knowledge (AK), which uses domain knowledge to extract and engineer new features present in the raw data and provides additional context that helps the model better understand and differentiate between legitimate and malicious data. The main goal of this research is to propose a method that uses Auxiliary Knowledge (AK) to improve ML performance in detecting cyberattacks. We leveraged relevant domain knowledge to generate features from the raw data that are difficult for an ML model to discover. This approach also reduces the dependence on large amount of training data (big data) that is necessary for better ML predictions. The experiments used the CICIoT2023 dataset and demonstrated that auxiliary knowledge improves the detection performance, paving the way for future integration of automated knowledge management approaches.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".