Paper Info
Title
Learning Security Classifiers with Verified Global Robustness Properties
Abstract
ABSTRACTMany recent works have proposed methods to train classifiers with local robustness properties, which can provably eliminate classes of evasion attacks for most inputs, but not all inputs. Since data distribution shift is very common in security applications, e.g., often observed for malware detection, local robustness cannot guarantee that the property holds for unseen inputs at the time of deploying the classifier. Therefore, it is more desirable to enforce global robustness properties that hold for all inputs, which is strictly stronger than local robustness. In this paper, we present a framework and tools for training classifiers that satisfy global robustness properties. We define new notions of global robustness that are more suitable for security classifiers. We design a novel booster-fixer training framework to enforce global robustness properties. We structure our classifier as an ensemble of logic rules and design a new verifier to verify the properties. In our training algorithm, the booster increases the classifier's capacity, and the fixer enforces verified global robustness properties following counterexample guided inductive synthesis. We show that we can train classifiers to satisfy different global robustness properties for three security datasets, and even multiple properties at the same time, with modest impact on the classifier's performance. For example, we train a Twitter spam account classifier to satisfy five global robustness properties, with 5.4% decrease in true positive rate, and 0.1% increase in false positive rate, compared to a baseline XGBoost model that doesn't satisfy any property.
Year
DOI
Venue
2021
10.1145/3460120.3484776
Computer and Communications Security
Keywords
DocType
Citations 
Verifiable Machine Learning, Security Classifier, Adversarial machine learning, Global Robustness Properties, Formal Verification
Conference
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Yizheng Chen1606.91
Shiqi Wang2166.25
Qin Yue36411.92
Xiaojing Liao49613.66
Suman Jana5110849.49
David Wagner612563933.74