Bayesian Learned Models Can Detect Adversarial Malware For Free

• URL: http://arxiv.org/abs/2403.18309v1
• Date: Wed, 27 Mar 2024 07:16:48 GMT
• Title: Bayesian Learned Models Can Detect Adversarial Malware For Free
• Authors: Bao Gia Doan, Dang Quang Nguyen, Paul Montague, Tamas Abraham, Olivier De Vel, Seyit Camtepe, Salil S. Kanhere, Ehsan Abbasnejad, Damith C. Ranasinghe,
• Abstract summary: Adversarial training is an effective method but is computationally expensive to scale up to large datasets. In particular, a Bayesian formulation can capture the model parameters' distribution and quantify uncertainty without sacrificing model performance. We found, quantifying uncertainty through Bayesian learning methods can defend against adversarial malware.
• Score: 28.498994871579985
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: The vulnerability of machine learning-based malware detectors to adversarial attacks has prompted the need for robust solutions. Adversarial training is an effective method but is computationally expensive to scale up to large datasets and comes at the cost of sacrificing model performance for robustness. We hypothesize that adversarial malware exploits the low-confidence regions of models and can be identified using epistemic uncertainty of ML approaches -- epistemic uncertainty in a machine learning-based malware detector is a result of a lack of similar training samples in regions of the problem space. In particular, a Bayesian formulation can capture the model parameters' distribution and quantify epistemic uncertainty without sacrificing model performance. To verify our hypothesis, we consider Bayesian learning approaches with a mutual information-based formulation to quantify uncertainty and detect adversarial malware in Android, Windows domains and PDF malware. We found, quantifying uncertainty through Bayesian learning methods can defend against adversarial malware. In particular, Bayesian models: (1) are generally capable of identifying adversarial malware in both feature and problem space, (2) can detect concept drift by measuring uncertainty, and (3) with a diversity-promoting approach (or better posterior approximations) lead to parameter instances from the posterior to significantly enhance a detectors' ability.

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