PAC-X: Fuzzy Explainable AI for Multiclass Malware Detection
Bibliographic record
Abstract
Researchers often approach malware detection as a binary classification problem. However, evidence indicates that malware can belong to multiple families simultaneously, and malicious files frequently exhibit numerous benign features. Attackers exploit this by embedding malicious intent within benign features, making malware detection a problem better suited for fuzzy systems. Furthermore, providing explainability for such fuzzy classification remains a significant challenge, requiring specialized Explainable AI (XAI) frameworks. Existing XAI approaches offer insights into model decisions but are vulnerable to adversarial attacks that manipulate features to mislead models. To address these issues, we propose PAC-X, a novel XAI framework for malware detection. PAC-X integrates the Conditional Attention Neural Network (CAN-Net) to deliver comprehensive multi-fuzzy-class explainability and employs Contextual Fuzzy Clustering (CFC) to extract contextual insights from training data. This framework is resilient to adversarial manipulations, maintaining reliable and interpretable explanations even under adversarial conditions designed to mislead the model. Through extensive evaluations on diverse malware datasets, PAC-X demonstrates superior explainability and robustness compared to state-of-the-art XAI methods. It provides a critical advancement in cybersecurity by addressing the complexities of evasive malware detection and enabling a deeper interpretation of multi-class malware characteristics.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".