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Efficient Android malware detection model DroidDet based on rotating forest

A malware and detection model technology, applied in the field of information security, can solve the problems of complexity, low accuracy, and high cost of human intervention in environment construction, achieving high efficiency and overcoming low accuracy.

Inactive Publication Date: 2017-07-04
XIJING UNIV
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AI Technical Summary

Benefits of technology

The technical effect of this patented innovation is improved performance by utilizing both statistical techniques like analyzed data from different sources or models trained on historical files obtained through previous researches. This allows for effective identification of potentially harmful applications based solely upon their behavior patterns without relying heavily on static methods which may have lower success rate due to factors like vulnerabilities being discovered during testing. Additionally, these improvements also allow users to easily extend them beyond what was previously possible because they were able to learn how well old programs are compared against older ones. Overall, this innovative approach improves security measures while reducing resource consumption and avoids potential issues caused by current technologies.

Problems solved by technology

This patents describes different technical problem addressed in this patent relating to improving software vulnerability against attacks made through various means like virus scanning mechanisms. Specifically, current techniques involve static analysis and dynamic analysis, making them challenging to identify threatening code embedded within executable programs. Dynamic analysis provides higher efficiency and lower costs over other approaches, allowing us to quickly respond to potential harmful exploits without compromise.

Method used

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  • Efficient Android malware detection model DroidDet based on rotating forest
  • Efficient Android malware detection model DroidDet based on rotating forest
  • Efficient Android malware detection model DroidDet based on rotating forest

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Embodiment Construction

[0036] The present invention will be further described below in conjunction with the accompanying drawings.

[0037] from figure 1 The overall framework of the present invention shows that the present invention mainly consists of two parts of work, one part is feature extraction and the other part is classifier. In the feature extraction part, use the apktool.jar decompilation tool to decompile the APK files of all Apps in the training set, then analyze and extract features such as permissions, system monitoring events, sensitive APIs, and permission rates, and perform PCA on these extracted features Processing to achieve normalization of features and retain the strongest principal components in the features to avoid noise interference in the extracted features. In addition, the Bootstrap self-service sampling method is used for sample interference to achieve the maximum diversity of the base learner. In the classifier part, an Android malware prediction model is constructed ...

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Abstract

The invention discloses an efficient Android malware detection model DroidDet based on rotating forest and belongs to the technical field of information security. According to the invention, a decompilation tool is used to decompile the Apk file in an Android application program and a combination of four groups of features is analyzed and extracted as the input for building a rotating forest classifier model. The model is characterized in that 1) the integrated learning method-rotating forest algorithm is initially adopted; further, each base learning device is trained through adopting PCA major constituent analysis and self-service sampling method and using a whole training set; accordingly, the model created in the invention has superior generalization performance; 2) the rotating forest algorithm has the feature of selecting the optimal characteristics so as to avoid noise data. The 10-fold cross-validation method is used for verifying the validity of the model created in the invention and the predicting accuracy of the model in the invention reaches 88.26%, which is 3.13% higher than the 84.93% of typical support vector machines. The model of the invention has wide application prospect in user information security.

Description

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Claims

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Application Information

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Owner XIJING UNIV
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