A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques

Joint Authors

Liu, Xiaojian
Lei, Qian
Liu, Kehong

Source

Mathematical Problems in Engineering

Issue

Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-15, 15 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2020-05-27

Country of Publication

Egypt

No. of Pages

15

Main Subjects

Civil Engineering

Abstract EN

An explosive spread of Android malware causes a serious concern for Android application security.

One of the solutions to detecting malicious payloads sneaking in an application is to treat the detection as a binary classification problem, which can be effectively tackled with traditional machine learning techniques.

The key factors in detecting Android malware with machine learning techniques are feature selection and generation.

Most of the existing approaches select and generate features without fully examining the structures of programs, and thus the important semantic information associated with these features is lost, consequently resulting in a low accuracy rate in detection.

To address this issue, we propose a new feature generation approach for Android applications, which takes components and program structures into consideration and extracts features in a graph-based and semantics-rich style.

This approach highlights two major distinguishing aspects: the context-based feature selection and graph-based feature generation.

We abstract an Android application as a collection of reduced iCFGs (interprocedural control flow graphs) and extract original features from these graphs.

Combining the original features and their contexts together, we generate new features which hold richer semantic information than the original ones.

By embedding the features into a feature vector space, we can use machine learning techniques to train a malware detector.

The experiment results show that this approach achieves an accuracy rate of 95.4% and a recall rate of 96.5%, which prove the effectiveness and advantages of our approach.

American Psychological Association (APA)

Liu, Xiaojian& Lei, Qian& Liu, Kehong. 2020. A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1194675

Modern Language Association (MLA)

Liu, Xiaojian…[et al.]. A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques. Mathematical Problems in Engineering No. 2020 (2020), pp.1-15.
https://search.emarefa.net/detail/BIM-1194675

American Medical Association (AMA)

Liu, Xiaojian& Lei, Qian& Liu, Kehong. A Graph-Based Feature Generation Approach in Android Malware Detection with Machine Learning Techniques. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-15.
https://search.emarefa.net/detail/BIM-1194675

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1194675