Novel Actionable Counterfactual Explanations for Intrusion Detection Using Diffusion Models
Bibliographic record
Abstract
Modern network intrusion detection systems (NIDSs) rely on complex deep learning models. However, the “black-box” nature of deep learning methods hinders transparency and trust in predictions, preventing the timely implementation of countermeasures against intrusion attacks. Although explainable AI (XAI) methods provide a solution to this problem by providing insights into the reasons behind the predictions, the explanations provided by the majority of them cannot be trivially converted into actionable countermeasures. In this work, we propose a novel tabular diffusion-based counterfactual explanation framework that can provide actionable explanations for network intrusion attacks. We evaluated our proposed algorithm against several other publicly available counterfactual explanation algorithms on three modern network intrusion datasets. To the best of our knowledge, this work also presents the first comparative analysis of the existing counterfactual explanation algorithms within the context of NIDSs. Our proposed method provides plausible and diverse counterfactual explanations more efficiently than the tested counterfactual algorithms, reducing the time required to generate explanations. We also demonstrate how the proposed method can provide actionable explanations for NIDSs by summarizing them into a set of actionable global counterfactual rules, which effectively filter out incoming attack queries. This ability of the rules is crucial for efficient intrusion detection and defense mechanisms. We have made our implementation publicly available on GitHub.
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 | 0.005 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".