Mecha: A Neural-Symbolic Open-Set Homogeneous Decision Fusion Approach for Zero-Day Malware Similarity Detection
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Bibliographic record
Abstract
With increasing numbers of novel malware each year, tools are required for efficient and accurate variant matching under the same family, for the purpose of effective proactive threat detection, retro-hunting, and attack campaign tracking. All of the state-of-the-art Deep Learning (DL) approaches assume that the incoming samples originate from known families and incorrectly identify novel families. Additionally, most of the existing solutions that leverage the Siamese Neural Network architecture either rely on pair-wise comparisons or computationally expensive preprocessing steps that are not scalable to a real-world malware triage volume requirement. We propose a different route, Mecha, a Neural-Symbolic Machine Learning (ML) system for malware variant matching and zero-day family detection. Mecha is comprised of an embedding network trained in two different scenarios for byte string embedding and an open-set approximate nearest neighbour algorithm for variant matching and zero-day detection. Our embedding network uses triplet loss for embedding generation and reinforcement-based Expectation Maximization (EM) learning for full deployment optimization. We conduct multiple in-sample and out-of-sample experiments to demonstrate the model's generalizability toward novel variants and families. We also show that Mecha can detect samples outside the known set of malware samples with an accuracy greater than 0.990.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it