An exploratory study on domain knowledge infusion in deep learning for automated threat defense
Bibliographic record
Abstract
Abstract The wide adoption of interconnected services leads to the creation of supportive solutions and business opportunities. Conversely, this new paradigm is targeted by malicious activities, aiming to compromise systems’ confidentiality, integrity, and availability. However, advanced methods lack contextual awareness, which prevents their deployment to real-world systems. Considering that the process of making informed decisions stems from the expertise of analysts based on their experience, the use of cybersecurity domain knowledge has the potential to improve Deep Learning and Deep Reinforcement Learning operations in real scenarios. Therefore, the main goal of this research is to study and evaluate the use of Knowledge Infused Learning in the context of automated threat defense. We define how cybersecurity domain knowledge can be infused into Deep Learning and Reinforcement Learning, highlighting the main challenges and benefits. Besides, we present a roadmap to apply domain knowledge for red and blue teaming activities and discuss the implications of Knowledge Infused Learning in explainability, and actionable reporting. Finally, we list the open challenges to guide the development of next-generation security solutions.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".