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Android platform malicious application detection method and device based on deep learning

A deep learning and malicious application technology, applied in platform integrity maintenance, computer security devices, instruments, etc., can solve the problems that users are difficult to distinguish between malicious applications and normal applications, and the permissions required by applications are single and one-sided, so as to improve security Effect

Active Publication Date: 2014-10-29
北京赋乐科技有限公司
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  • Abstract
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AI Technical Summary

Problems solved by technology

In fact, because the "permission required by the application" prompted by this technology is too single and one-sided, it is difficult for ordinary users to quickly distinguish whether it is a malicious application based on this information alone.
Practice shows that many malicious applications and normal applications are likely to require the same permissions, which makes it more difficult for users to distinguish between malicious applications and normal applications

Method used

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  • Android platform malicious application detection method and device based on deep learning
  • Android platform malicious application detection method and device based on deep learning
  • Android platform malicious application detection method and device based on deep learning

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Embodiment Construction

[0052] In order to solve the problem of detecting malicious applications on the Android platform, the present invention provides an Android application detection method and device based on a deep learning model, which mainly includes the following four steps: S1. Android application original installation file feature extraction; S2. Android application installation Running feature extraction; S3. Android application deep learning model establishment; S4. Android normal application and malicious application identification. The present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0053] method embodiment

[0054] According to an embodiment of the present invention, a method for detecting Android malicious applications based on a deep learning model is provi...

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Abstract

The invention discloses an Android platform malicious application detection method and device based on deep learning. The method comprises the first step of extracting Android application original installation files, the second step of extracting Android application installation operating features, the third step of setting up an Android application deep learning model and the fourth step of recognizing Android normal applications and Android malicious applications.

Description

technical field [0001] The invention relates to feature extraction and deep learning technology, in particular to a method for applying deep learning to Android malicious application detection. Background technique [0002] With the rapid development of smart phones and mobile devices, Android platform services have become an indispensable element for most network users. At the same time, mobile malware is also rapidly growing to become an important source of threats to network security and privacy. According to a recent research report from Gartner, Android tablet sales grew by 127% in 2013, occupying the first place in the overall mobile tablet market share. Therefore, malicious application detection under Android has become an important technical guarantee for the development of mobile Internet today. Researching and realizing high-precision Android malicious application detection has strong practical significance and practical value, and has attracted the attention of r...

Claims

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Application Information

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IPC IPC(8): G06F21/56
CPCG06F21/562G06F21/566
Inventor 卢永强袁振龙
Owner 北京赋乐科技有限公司
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