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Machine learning malicious software detection method based on privilege features and taint features

A malware and machine learning technology, applied in the field of information security, can solve problems such as leaking permissions, reducing the performance and occupation of mobile phones, and achieving the effect of reducing volume and improving classification results.

Pending Publication Date: 2022-04-12
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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  • Abstract
  • Description
  • Claims
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AI Technical Summary

Problems solved by technology

However, running dynamic analysis as a service will inevitably occupy additional memory and resources of the system, reduce the performance of the mobile phone, and when the dynamic monitoring program is attacked, it will expose sensitive information and leak higher-level permissions

Method used

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  • Machine learning malicious software detection method based on privilege features and taint features
  • Machine learning malicious software detection method based on privilege features and taint features
  • Machine learning malicious software detection method based on privilege features and taint features

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Embodiment Construction

[0043] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary only for explaining the present invention and should not be construed as limiting the present invention.

[0044] Such as figure 1 As shown, the present invention provides a kind of machine learning malware detection method based on privilege feature and taint feature, specifically comprises the following steps:

[0045] Step 1, decompress the APK file and obtain the AndroidMainfest.xml file;

[0046] Step 2, decompile the AndroidMainfest.xml file, and extract permission information from it;

[0047] Step 3, use FlowDroid to extract sink features, source features and path features;

[0048] Step 4, calculate the frequency of occurrence of each permission, and obtain the characteristic value table of a single permission;

[0049] First extract M kinds of permissions with the...

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Abstract

The invention discloses a machine learning malicious software detection method based on privilege features and taint features, which is characterized in that an existing detection scheme based on taint analysis is improved based on the privilege features, a high-classification result can be realized while a small data volume is used, and through multi-dimensional calculation of extended feature values, the detection accuracy is improved. According to the method, a feature value table based on privilege features, taint source features and taint sink features is constructed, Transform is introduced to analyze and detect the feature value table, and malicious software detection based on an Android platform is well achieved.

Description

technical field [0001] The invention relates to a machine learning malicious software detection method based on privilege features and taint features, and belongs to the technical field of information security. Background technique [0002] With the rapid development of mobile devices, the security issues of application software are becoming more and more prominent. Even apps downloaded from official app stores have many problems, and these apps access some sensitive information, such as the device's location, contacts, call history and IMEI (International Mobile Equipment Identity). In addition, social-related applications and banking-related applications also collect and store a large amount of sensitive data and private information, such as chat records in social software, bank passwords in banking-related applications, etc. The consequences of these actions, whether intentional or not, are unpredictable. Sensitive data leaks from popular apps have been widely acknowled...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F21/56G06F8/53
Inventor 方黎明朱泽升恽昕宇
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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