Android malicious application detection method and system based on multi-feature fusion

A malicious application, multi-feature fusion technology, applied in the field of network security, can solve problems such as high-dimensional feature analysis of rare sample families of malicious code, achieve efficient fusion processing, prevent overfitting, and reduce interference.

Active Publication Date: 2017-09-19
BEIJING INSTITUTE OF TECHNOLOGYGY
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[0008] In view of this, the present invention provides a method and system for detecting Android malicious applications based on

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  • Android malicious application detection method and system based on multi-feature fusion
  • Android malicious application detection method and system based on multi-feature fusion
  • Android malicious application detection method and system based on multi-feature fusion

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Embodiment Construction

[0052] The present invention will be described in detail below with reference to the accompanying drawings and examples.

[0053] The present invention proposes a detection scheme for Android malicious applications based on multi-feature fusion, and its basic idea is: decompile Android malicious application samples to obtain decompiled apk files; extract static data from decompiled apk files Features; extract dynamic features by running the apk file in the Android emulator; for static features and dynamic features, use the text hash algorithm part of the local sensitive hash algorithm to perform feature mapping, and map to the low-dimensional feature space to obtain the fused Feature vectors; finally, based on the fused feature vectors, a classifier is trained using a machine learning classification algorithm; the classifier is used to classify and detect Android malicious applications.

[0054] In order to achieve the above solution, the present invention provides a multi-fea...

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Abstract

The invention discloses an Android malicious application detection method and system based on multi-feature fusion. The method comprises the following steps that: carrying out decompilation on an Android application sample to obtain a decompilation file; extracting static features from the decompilation file; operating the Android application sample in an Android simulator to extract dynamic features; carrying out feature mapping on the static features and the dynamic features by the text Hash mapping part of a locality sensitive Hash algorithm, mapping to a low-dimensional feature space to obtain a fused feature vector; and on the basis of the fused feature vector, utilizing a machine learning classification algorithm to train to obtain a classifier, and utilizing the classifier to carry out classification detection. By use of the method, the high-dimensional feature analysis problem of a malicious code rare sample family can be solved, and detection accuracy is improved.

Description

technical field [0001] The invention relates to the technical field of network security, in particular to a multi-feature fusion-based Android malicious application detection method and system. Background technique [0002] With the hot sales of smartphones, mobile Internet access is becoming more and more popular, and the outbreak of the mobile Internet era has driven smartphones to become more versatile. With the explosive growth of mobile phone users and the convenience of mobile Internet access, mobile phone security risks are becoming more and more serious. More and more problems are becoming more and more prominent. While mobile smartphones create convenience for users, they also bring new development directions for malicious code attacks on mobile phones. The large amount of user personal privacy data stored in mobile phones and the potential huge economic benefits of mobile Internet have made hackers and malware creators take smartphones as new attack targets. [00...

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Application Information

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IPC IPC(8): G06F21/56G06N99/00G06K9/62
CPCG06F21/562G06F21/566G06N20/00G06F18/253
Inventor 薛静锋张继蔡建宇彭图王勇
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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