Core Concepts
Bayesian models can effectively detect adversarial malware by leveraging uncertainty without sacrificing performance.
Abstract
The article discusses the vulnerability of machine learning-based malware detectors to adversarial attacks and proposes a Bayesian approach to detect adversarial malware. It explores the concept of epistemic uncertainty in machine learning-based malware detectors and how Bayesian models can quantify uncertainty to defend against adversarial malware. The study covers Android, Windows, and PDF malware domains, highlighting the effectiveness of Bayesian models in detecting adversarial malware.
Introduction:
Malware incidents are on the rise, posing significant challenges.
Machine learning has improved malware detection but is vulnerable to adversarial attacks.
Adversarial malware deceives ML-based detectors by misclassifying malware as benignware.
Problem:
Adversarial training is effective but costly and compromises model performance.
Adversarial malware exploits low-confidence regions of ML models.
Epistemic uncertainty in ML detectors arises from a lack of training samples in certain regions.
Approach:
Bayesian learning captures model parameter distribution and quantifies uncertainty.
Mutual information is used to measure uncertainty and detect adversarial malware.
Bayesian models defend against adversarial malware without performance compromise.
Experiments and Results:
Clean performance evaluation in Android domain shows Bayesian models outperform FFNN.
Robustness against problem-space and feature-space adversarial attacks demonstrates Bayesian models' effectiveness.
Generalization to PDF and Windows PE malware domains shows Bayesian models' superiority.
Concept Drift:
Bayesian models can detect concept drift by measuring uncertainty, aiding in timely detection of evolving malware.
Model Parameter Diversity Measures:
Diversity among parameter particles is measured using KL Divergence, showing SVGD enhances diversity for improved performance.
Threat to Validity:
Uncertainty estimates from Bayesian models may be inaccurate due to model under-specifications.
Calibration methods can improve uncertainty estimates.
Conclusion:
Bayesian models effectively detect adversarial malware by leveraging uncertainty.
Future research should focus on improving posterior approximations for robust malware defense strategies.
Stats
Adversarial training is shown to be non-trivial for large-scale datasets.
Bayesian models can detect adversarial malware effectively.
Bayesian models are versatile and adaptable to various malware domains.
Quotes
"Adversarial training is effective but compromises model performance necessary for robustness."
"Bayesian models can defend against adversarial malware without sacrificing detection performance."