Efficient Android malware detection model DroidDet based on rotating forest

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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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.

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