DroidEye: Fortifying Security of Learning-Based Classifier Against Adversarial Android Malware Attacks

Chen, Lingwei; Hou, Shifu; Ye, Yanfang; Xu, Shouhuai

doi:10.1109/asonam.2018.8508284

Cited by 29 publications

(25 citation statements)

References 15 publications

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“…• We explore the arms race between adversarial malware attack and defense to en-hance the security of machine learning-based detection systems through analyzing adversarial attacks, and formulating secure-learning paradigms to counter the adversarial attacks. Our work on adversarial machinle learning in malware detection has resulted in 5 publications [32,33,28,29,30].…”

Section: Contributions Of This Dissertationmentioning

confidence: 99%

“…DroidEye to counter these attacks. In our proposed methods, SecDefender adopts classifier retraining technique on basis of our proposed adversarial attack model AdvAttack and enhances the robustness of the classifier using the security regularization terms; SecureDroid utilizes a novel feature selection method to build more secure classifier by enforcing attackers to increase the adversarial costs and maximize the manipulations, and introduces an ensemble learning approach to aggregate different individual classifiers constructed using our proposed feature selection method to improve system security while not compromising detection accuracy; DroidEye takes advantage of gradient masking for feature space, utilizes count featurization to transform the binary feature space into continuous probabilities encoding the distribution in each class (either benign or malicious) to reduce the adversarial gradient of the learning model, and then introduces softmax function (i.e., normalized exponential function) with adversarial parameter to find the best trade-off between security and accuracy for the classifier by tuning the adversarial parameter [32,33,28,30] (See Section 4.3 for details).…”

Section: Contributions Of This Dissertationmentioning

confidence: 99%

“…• We develop practical systems integrating our proposed methods for comprehensive experimental studies on real sample collections from an anti-malware industry company. Specifically, we collect different sample sets from Comodo Cloud Security Center; based on these real sample collections, we construct practical systems based on our proposed methods and provide a series of comprehensive experiments to empirically evaluate the performances of these methods [33,28,32,31,27,30] (See Section 3.2.3, 3.3.3, 4.3.4, 4.4.4, and 4.5.3 for details).…”

Section: Contributions Of This Dissertationmentioning

confidence: 99%

“…In sum, by the date of this dissertation, we have had 9 publications [31,27,66,143,32,33,28,29,30] in intelligent malware detection using file-to-file relations and adversarial machine learning in malware detection, including the prestigious IEEE EISIC'2017 Best…”

Section: Contributions Of This Dissertationmentioning

confidence: 99%

“…Feature Integration (denoted as f I ). The feature integration of an ensemble is the percentage of features that are included in at least one of the base classifiers, which can be defined as follow: 30) where the feature set F ∈ R d in the ensemble is aggregated by the feature sets F k (k = 1, 2, ..., K) from base classifiers.…”

Section: Ensemble Learning To Improve Detection Accuracymentioning

confidence: 99%

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Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks

Chen¹

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Intelligent Malware Detection Using File-to-file Relations and Enhancing its Security against Adversarial Attacks Lingwei Chen With computing devices and the Internet being indispensable in people's everyday life, malware has posed serious threats to their security, making its detection of utmost concern. To protect legitimate users from the evolving malware attacks, machine learning-based systems have been successfully deployed and offer unparalleled flexibility in automatic malware detection. In most of these systems, resting on the analysis of different content-based features either statically or dynamically extracted from the file samples, various kinds of classifiers are constructed to detect malware. However, besides content-based features, file-to-file relations, such as file coexistence , can provide valuable information in malware detection and make evasion harder. To better understand the properties of file-to-file relations, we construct the file coexistence graph. Resting on the constructed graph, we investigate the semantic relatedness among files, and leverage graph inference, active learning and graph representation learning for malware detection. Comprehensive experimental results on the real sample collections from Comodo Cloud Security Center demonstrate the effectiveness of our proposed learning paradigms. As machine learning-based detection systems become more widely deployed, the incentive for defeating them increases. Therefore, we go further insight into the arms race between adversarial malware attack and defense, and aim to enhance the security of machine learning-based malware detection systems. In particular, we first explore the adversarial attacks under different scenarios (i.e., different levels of knowledge the attackers might have about the targeted learning system), and define a general attack strategy to thoroughly assess the adversarial behaviors. Then, considering different skills and capabilities of the attackers, we propose the corresponding secure-learning paradigms to counter the adversarial attacks and enhance the security of the learning systems while not compromising the detection accuracy. We conduct a series of comprehensive experimental studies based on the real sample collections from Comodo Cloud Security Center and the promising results demonstrate the effectiveness of our proposed securelearning models, which can be readily applied to other detection tasks. Contents Acknowledgments iii List of Figures vi List of Tables ix List of Notations x List of Acronyms xii List of Figures vii 4.2 Different scenarios of the adversarial attacks. With the direction of the inward arrow, the adversarial attacks are depicted with the knowledge of (X,D), (X, D), and (X, D, f

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