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Detection of Malicious PDF Files Using a Two-Stage Machine Learning Algorithm

Chinese Journal of Electronics 2020年第6期29卷 1165-1177页

作者： HE Kang ZHU Yuefei HE Yubo LIU Long LU Bin LIN Wei State Key Laboratory of Mathematical Engineering and Advanced Computing

Portable document format(PDF) files are increasingly used to launch cyberattacks due to their popularity and increasing number of *** solutions have been developed to detect malicious files,but their accuracy decreases rapidly in face of new evasion *** explore how to improve the robustness of classifiers for detecting adversarial attacks in PDF *** replacement and the n-gram are implemented to extract robust features using proposed guiding *** the two-stage machine learning model,the objects are divided based on their types,and the anomaly detection model is first trained for each type *** former detection results are organized into tree-like information structure and treated as inputs to convolutional neural *** results show that the accuracy of our classifier is nearly 100% and the robustness against evasive samples is *** object features also enable the identification of different vulnerabilities exploited in malicious PDF files.

关键词： convolutional neural nets document handling feature extraction learning (artificial intelligence) pattern classification security of data trees (mathematics) robust feature extraction convolutional neural network tree-like information structure adversarial attack detection two-stage machine learning algorithm evasion techniques malicious files portable document format malicious PDF files classifier robustness anomaly detection model two-stage machine learning model Malicious PDF classification robustness Guiding principles Two-stage classifier Convolutional neural network

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