Bibliographic record
Abstract
Program and malware analysis tools are important for computer security. Recently, machine learning has been applied to help with program and malware analysis. In this thesis, we investigate a few areas in which the application of machine learning can be used to improve program analysis. We find several cases where understanding the problem domain of program analysis can be more important than achieving raw machine learning capabilities on its own. The first application is symbolic analysis. We use path features instead of constraint features in related work to train models that predict whether paths are possible in symbolic analysis. These statistical path features can be applied without constraint collection. With these features, simple machine learning models are enough to save time, and their predictions can generalize across software applications. Also, we examine the sometimes complex interactions between factors such as model performance, design of symbolic analysis tool, distribution of path analysis time, and the algorithm that prioritizes and selects paths for symbolic analysis. We find that leveraging domain-specific knowledge about analyzed paths can achieve better time savings than improving model performance. Another application is malware classification. While machine learning models can detect malware, these models need to be frequently retrained to ensure they are up to date. Data drift detectors select drifting software samples into the datasets for retraining models, so model performance does not decrease significantly upon changes in software data. However, how malware samples can evade these data drift detectors during malware classification has not been studied. We examine how evasion and poisoning attacks work against data drift detectors and malware classifiers. We find how unique properties from the underlying models of the data drift detectors cause some attacks that work against malware classification to fail against data drift detection. With improper attack methods, a more capable attacker can even decrease the chance of attack success against data drift detection. Finally, we also propose a poisoning attack that properly works against one such data drift detector for retraining malware classifiers.
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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 | 0.004 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".