Design And Implementation Of Android Software Behavior Analysis System

With the increase of the popularity of Android mobile phone, the number of Android malware is growing at an alarming speed. In order to protect the privacy and property safety of Android users, it is a key issue to implement Android malware detection. Behavior-based Android malware detection has drawn a lot of people’s attention because of its accuracy, and becomes a promising approach. Based on this background, we decide to design and implement an Android software behavior analysis system.In the first place, we introduce the background and task of this thesis, present the related work of Android software behavior analysis, and describe the technical background of the system, including shared library injection, hooking, Binder mechanism and machine learning. Next, we introduce the requirements analysis from two aspects, namely functional requirements and non-functional requirements. Then, we divide the system into two parts, namely behavior interception module and behavior analysis module, and carry out summary design and particular design on both of them with flow charts. Finally, we introduce the experimental results; our experiments are conducted with1136real-world samples and three standard metrics are introduced to evaluate the experimental results.This system shows the effectiveness of Android malware detection, and the contributions of it are as follows:in the technical aspect, we effectively combine three techniques together, namely shared library injection, hooking and machine learning; in the theoretical aspect, we propose a feature weighting method to improve the classification effect of SVM. With the combination of engineering innovation and theoretical researches, we carry out an effective Android software behavior analysis system.