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A method for judging abnormality of app software running data

A technology for operating data and abnormal judgment, applied in the direction of electrical digital data processing, hardware monitoring, instruments, etc., can solve the problem that training samples are not updated in time, not combined with system log information, system resource consumption status, APP software resource consumption status, and impact judgment The accuracy of method identification and other issues

Active Publication Date: 2021-03-02
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] At present, the method for judging APP software abnormalities is mainly to implement a lightweight judgment method for a large number of samples. Basically, machine learning methods are used to establish a data training model, and then judge the running data of APP software, without combining system log information and system resource consumption status. and APP software resource consumption status, and the update of training samples is not timely enough, which will affect the accuracy of the judgment method recognition

Method used

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  • A method for judging abnormality of app software running data
  • A method for judging abnormality of app software running data
  • A method for judging abnormality of app software running data

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Embodiment 1

[0041] Embodiment 1: as Figure 1-3 As shown, a method for judging the abnormality of APP software operation data, the specific steps of the method are as follows:

[0042] APP software operation data set U is made up of 7 pieces of data in the present embodiment, as shown in Table 1, U={u 1 ,u 2 ,...,u n}, where u i =(UTime i ,UPid i ,Rcpu i ,Rmem i ,ProNum i ,SerNum i ,Smem i ,Scpu i , U_flag i )(i=1,2,...,n) indicates the i-th APP software data information, UTime i Indicates the current time of the system in the i-th APP software data information; UPid i Represents the APP software process number running in the i-th APP software data information; Rcpu i Indicates the CPU size occupied by the APP software in the i-th APP software data information; Rmem i Indicates the memory size occupied by the APP software in the i-th APP software data information; ProNum i Indicates the number of system processes in the i-th APP software data information; SerNum i Indicat...

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Abstract

The invention relates to a method for judging abnormality of APP software running data, belonging to the field of APP software running detection. The method of the present invention is as follows: taking the APP software operation data set as input, marking the APP software operation data set based on the system log, judging the data abnormality in the APP software operation data set based on the SVM, and outputting the judgment result. The invention combines the mobile phone log information with the SVM algorithm, which helps to more accurately judge whether there is abnormality in the APP application program data information; the invention optimizes the relevant parameters of the SVM, which helps to improve the accuracy of judgment; The SVM training data set is expanded to help improve the accuracy of judgment.

Description

technical field [0001] The invention relates to a method for judging abnormality of APP software running data, belonging to the field of APP software running detection. Background technique [0002] The APP software abnormality judgment method under the Android platform mainly adopts the machine learning method to construct the training data model, and finally judges whether the software is abnormal through the constructed model. For example, Sun Min and others used the feature-weighted K-nearest neighbor method to simplify the SVM training set and construct a classifier. Liu Xiaoming and others proposed a method of only using benign samples as a training set, and then using the nearest neighbor (KNN) machine learning algorithm to establish a benign application behavior model to realize the method of abnormal judgment of APP software. [0003] At present, the method for judging APP software abnormalities is mainly to implement a lightweight judgment method for a large numbe...

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06F11/34
CPCG06F11/3452G06F11/3476G06F2201/865
Inventor 姜瑛徐玉强李凌宇刘英莉丁家满汪海涛
Owner KUNMING UNIV OF SCI & TECH
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