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• W2893090392 abstract "Android has become the most popular platform for mobile devices, and also it has become a popular target for malware developers. At the same time, researchers have proposed a large number of methods, both static and dynamic analysis methods, to fight against malwares. Among these, Machine learning based methods are quite effective in Android malware detection, the accuracy of which can be up to 98%. Thus, malware developers have the incentives to develop more advanced malwares to evade detection. This paper presents an adversary attack pattern that will compromise current machine learning based malware detection methods. The malware developers can perform this attack easily by splitting malicious payload into two or more apps. The split apps will all be classified as benign by current methods. Thus, we proposed a method to deal with this issue. This approach, realized in a tool, called ColluDroid, can identify the collusion apps by analyzing the communication between apps. The evaluation results show that ColluDroid is effective in finding out the collusion apps. Also, we showed that it’s easy to split an app to evade detection. According to our split simulation, the evasion rate is 78%, when split into two apps; while the evasion rate comes to 94.8%, when split into three apps." @default.
• W2893090392 created "2018-10-05" @default.
• W2893090392 creator A5018349649 @default.
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• W2893090392 date "2018-01-01" @default.
• W2893090392 modified "2023-09-23" @default.
• W2893090392 title "Malware Collusion Attack Against Machine Learning Based Methods: Issues and Countermeasures" @default.
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• W2893090392 doi "https://doi.org/10.1007/978-3-030-00018-9_41" @default.
• W2893090392 hasPublicationYear "2018" @default.
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