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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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Delay-CJ:A novel cryptojacking covert attack method based on delayed strategy and its detection

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Digital Communications and Networks 2023年第5期9卷 1169-1179页

作者： Guangquan Xu Wenyu Dong Jun Xing Wenqing Lei Jian Liu Lixiao Gong Meiqi Feng Xi Zheng Shaoying Liu Big Data School Qingdao Huanghai UniversityQingdao266427China College of Intelligence and Computing Tianjin UniversityTianjin300350China School of Computing Macquarie UniversityNSW2109Australia Graduate School of Advanced Science and Engineering Hiroshima UniversityJapan

Cryptojacking is a type of resource embezzlement attack,wherein an attacker secretly executes the cryptocurrency mining program in the target host to gain *** has been common since 2017,and in fact,it once became the greatest threat to network *** better prove the attack ability the harm caused by cryptojacking,this paper proposes a new covert browser-based mining attack model named Delay-CJ,this model was deployed in a simulation environment for *** on the general framework of cryptojacking,Delay-CJ adds hybrid evasion detection techniques and applies the delayed execution strategy specifically for video websites in the prototype *** results show that the existing detection methods used for testing may become invalid as result of this *** view of this situation,to achieve a more general and robust detection scheme,we built a cryptojacking detection system named CJDetector,which is based on cryptojacking process ***,it identifies malicious mining by monitoring CPU usage and analyzing the function call *** system not only effectively detects the attack in our example but also has universal *** recognition accuracy of CJDetector reaches 99.33%.Finally,we tested the web pages in Alexa 50K websites to investigate cryptojacking activity in the real *** found that although cryptojacking is indeed on the decline,it remains a part of network security threats that cannot be ignored.

关键词： Cryptojacking evasion techniques Delayed strategy Cryptocurrency mining Behavior-based detection

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