Android platform callback function detecting method based on machine learning method

A callback function and machine learning technology, applied in platform integrity maintenance, instruments, computer parts, etc., can solve problems such as low accuracy and recall rate, poor universality, and inability to apply regular expressions to the Android operating system. Improve precision and recall, avoid omissions and errors, and improve recall

Active Publication Date: 2017-06-13
KUNSHAN BRANCH INST OF MICROELECTRONICS OF CHINESE ACADEMY OF SCI
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Problems solved by technology

However, regular expressions cannot be applied to different versions of the Android operating system. At the same time,

Method used

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  • Android platform callback function detecting method based on machine learning method
  • Android platform callback function detecting method based on machine learning method

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Embodiment Construction

[0036] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure may be embodied in various forms and should not be limited by the embodiments set forth herein. Rather, these embodiments are provided for more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art.

[0037] According to the embodiment of the present invention, propose a kind of Android platform callback function detection method based on machine learning method, such as figure 1 shown. The methods include:

[0038] Step S1: Obtain a data set, which is an Android platform framework package to be analyzed and stored in a non-volatile memory in the form of a compressed package;

[0039] Step S2: Preprocessing the acquired data s...

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Abstract

The invention discloses an Android platform callback function detecting method based on a machine learning method. The method includes the following steps that data sets are obtained; the obtained data sets are pre-processed; feature sets are established by using specific syntax and semantics of an Android platform, and the feature sets serves as feature vectors of the machine learning algorithm; the data sets are randomly selected to form a training set, and callback functions in the training set are manually labeled; a classifier is trained by using the training set, and a classifier model is obtained; according to the classifier model, the machine learning algorithm is adopted, all functions in the data sets are mapped to callback function sets or non-callback function sets respectively, and the identification of the callback functions is completed. The method takes the advantage of the machine learning algorithm, the callback functions and non-callback functions of the Android platform can thus be effectively and accurately identified, and the method is suitable for Android operating systems different in version.

Description

technical field [0001] The invention relates to the field of computer software, in particular to a method for detecting a callback function of an Android platform based on a machine learning method. Background technique [0002] With the rapid development of mobile application software based on the Android platform, a large number of malicious software targeting the Android operating system also follows. Malware is a kind of special software with malicious tendencies, which can endanger the security of the operating system and threaten the privacy of user data. As early as August 2010, Trojan horse viruses FakePlayer and DroidSMS based on the Android operating system began to appear, and then more malicious software became popular. These malware are not limited to stealing users' personal information, including geographical location, contact information, photos, voice, text messages, etc., but also perform various malicious operations such as making toll calls, recording ca...

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Application Information

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IPC IPC(8): G06F21/56G06K9/62
CPCG06F21/566G06F18/285G06F18/214
Inventor 陈秀鹏牟荣增王宏飞颜昊霖
Owner KUNSHAN BRANCH INST OF MICROELECTRONICS OF CHINESE ACADEMY OF SCI
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