An Android Malware Detection Model Based on DT-SVM

Min Yang,Yonggang Luo,Xingshu Chen,Hang Zhang,Zhe-Li Liu

doi:10.1155/2020/8841233

Min Yang, Yonggang Luo + Show 3 more

Open Access

https://doi.org/10.1155/2020/8841233

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Abstract

In order to improve the accuracy and efficiency of Android malware detection, an Android malware detection model based on decision tree (DT) with support vector machine (SVM) algorithm (DT-SVM) is proposed. Firstly, the original opcode, Dalvik opcode, is extracted by reversing Android software, and the eigenvector of the sample is generated by using the n-gram model. Then, a decision tree is generated via training the sample and updating decision nodes as SVM nodes from the bottom up according to the evaluation result of the test set in the decision path. The model effectively combines DT with SVM. Under the premise of maintaining a high-accuracy decision path, SVM is used to effectively reduce the overfitting problem in DT and thus improve the generalization ability, and maintain the superiority of SVM for the small sample training set. Finally, to test our approach, several simulation experiments are carried out, and the results demonstrate that the improved algorithm has better accuracy and higher speed as compared with other malware detection approaches.

Highlights

In recent years, mobile Internet has played a leading role in the evolvement of the Internet, and smartphones have become almost an indispensable tool in people’s daily life
E results show that the Precision, ACC, Recall, and F1 of the DT with the Support Vector Machine (SVM) algorithm (DT-SVM) algorithm are apparently higher than traditional DT and slightly higher than SVM
SVM takes the longest time, while DT-SVM is trained by DT first, and the small sample is trained by the SVM node

Summary

Introduction

Mobile Internet has played a leading role in the evolvement of the Internet, and smartphones have become almost an indispensable tool in people’s daily life. Smartphone penetration among adults in developed countries will reach 90 percent by the end of 2023, compared with 85 percent in 2018, and global smartphone sales will reach 1.85 billion units, 19% increase over 2018 [1]. According to [2], worldwide sales of smartphones to end users are on track to reach 1.57 billion units in 2020, an increase of 3% year over year. E amount of malware continues to grow at a faster rate each year and poses a serious security threat, antivirus vendors detect thousands of new malware samples daily, and there is still no end in sight [7]. With the gradual maturity of 5G technology, which marks the arrival of the era of intelligent networking and industrial Internet, the Internet of everything will lead to more lethal and wider harm caused by malware, and Security and Communication Networks malware detection has been and will be a critical topic in computer security

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