Multilevel permission extraction in android applications for malware detection

Zhen Wang, Kai Li, Yan Hu, Akira Fukuda, Weiqiang Kong

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

With the widespread use of Android applications in security-sensitive scenarios, more and more Android malware has been discovered. Existing work on malware detection fail to automatically learn effective feature interactions, which are, however, the key to the success of many prediction models. In order to detect malware efficiently and accurately, in this paper, we propose Multilevel Permission Extraction, an approach to automatically identify permission interactions that are effective in distinguishing between malicious and benign applications. We then utilize the extracted information to classify malicious and benign applications by machine learning based classification algorithms. We evaluate our approach in a large data set consisting of 4,868 benign applications and 4,868 malicious applications. The experimental results show that our malware detection approach can achieve over 95.8% in accuracy, precision, recall, and F-Score. Compared with two state-of-the-art approaches, we can achieve a better malware detection rate of 97.88%.

Original languageEnglish
Title of host publicationCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems
EditorsMohammad S. Obaidat, Zhenqiang Mi, Kuei-Fang Hsiao, Petros Nicopolitidis, Daniel Cascado-Caballero
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781538640883
DOIs
Publication statusPublished - Aug 2019
Event2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019 - Beijing, China
Duration: Aug 28 2019Aug 31 2019

Publication series

NameCITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems

Conference

Conference2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019
CountryChina
CityBeijing
Period8/28/198/31/19

Fingerprint

Learning systems
Malware
Interaction
Scenarios
Prediction model
Machine learning

All Science Journal Classification (ASJC) codes

  • Computer Vision and Pattern Recognition
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality
  • Computer Networks and Communications
  • Hardware and Architecture

Cite this

Wang, Z., Li, K., Hu, Y., Fukuda, A., & Kong, W. (2019). Multilevel permission extraction in android applications for malware detection. In M. S. Obaidat, Z. Mi, K-F. Hsiao, P. Nicopolitidis, & D. Cascado-Caballero (Eds.), CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems [8862060] (CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CITS.2019.8862060

Multilevel permission extraction in android applications for malware detection. / Wang, Zhen; Li, Kai; Hu, Yan; Fukuda, Akira; Kong, Weiqiang.

CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems. ed. / Mohammad S. Obaidat; Zhenqiang Mi; Kuei-Fang Hsiao; Petros Nicopolitidis; Daniel Cascado-Caballero. Institute of Electrical and Electronics Engineers Inc., 2019. 8862060 (CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Wang, Z, Li, K, Hu, Y, Fukuda, A & Kong, W 2019, Multilevel permission extraction in android applications for malware detection. in MS Obaidat, Z Mi, K-F Hsiao, P Nicopolitidis & D Cascado-Caballero (eds), CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems., 8862060, CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems, Institute of Electrical and Electronics Engineers Inc., 2019 International Conference on Computer, Information and Telecommunication Systems, CITS 2019, Beijing, China, 8/28/19. https://doi.org/10.1109/CITS.2019.8862060
Wang Z, Li K, Hu Y, Fukuda A, Kong W. Multilevel permission extraction in android applications for malware detection. In Obaidat MS, Mi Z, Hsiao K-F, Nicopolitidis P, Cascado-Caballero D, editors, CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems. Institute of Electrical and Electronics Engineers Inc. 2019. 8862060. (CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems). https://doi.org/10.1109/CITS.2019.8862060
Wang, Zhen ; Li, Kai ; Hu, Yan ; Fukuda, Akira ; Kong, Weiqiang. / Multilevel permission extraction in android applications for malware detection. CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems. editor / Mohammad S. Obaidat ; Zhenqiang Mi ; Kuei-Fang Hsiao ; Petros Nicopolitidis ; Daniel Cascado-Caballero. Institute of Electrical and Electronics Engineers Inc., 2019. (CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems).
@inproceedings{6330ad74e57d43abbbdb6cb677447b70,
title = "Multilevel permission extraction in android applications for malware detection",
abstract = "With the widespread use of Android applications in security-sensitive scenarios, more and more Android malware has been discovered. Existing work on malware detection fail to automatically learn effective feature interactions, which are, however, the key to the success of many prediction models. In order to detect malware efficiently and accurately, in this paper, we propose Multilevel Permission Extraction, an approach to automatically identify permission interactions that are effective in distinguishing between malicious and benign applications. We then utilize the extracted information to classify malicious and benign applications by machine learning based classification algorithms. We evaluate our approach in a large data set consisting of 4,868 benign applications and 4,868 malicious applications. The experimental results show that our malware detection approach can achieve over 95.8{\%} in accuracy, precision, recall, and F-Score. Compared with two state-of-the-art approaches, we can achieve a better malware detection rate of 97.88{\%}.",
author = "Zhen Wang and Kai Li and Yan Hu and Akira Fukuda and Weiqiang Kong",
year = "2019",
month = "8",
doi = "10.1109/CITS.2019.8862060",
language = "English",
series = "CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Obaidat, {Mohammad S.} and Zhenqiang Mi and Kuei-Fang Hsiao and Petros Nicopolitidis and Daniel Cascado-Caballero",
booktitle = "CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems",
address = "United States",

}

TY - GEN

T1 - Multilevel permission extraction in android applications for malware detection

AU - Wang, Zhen

AU - Li, Kai

AU - Hu, Yan

AU - Fukuda, Akira

AU - Kong, Weiqiang

PY - 2019/8

Y1 - 2019/8

N2 - With the widespread use of Android applications in security-sensitive scenarios, more and more Android malware has been discovered. Existing work on malware detection fail to automatically learn effective feature interactions, which are, however, the key to the success of many prediction models. In order to detect malware efficiently and accurately, in this paper, we propose Multilevel Permission Extraction, an approach to automatically identify permission interactions that are effective in distinguishing between malicious and benign applications. We then utilize the extracted information to classify malicious and benign applications by machine learning based classification algorithms. We evaluate our approach in a large data set consisting of 4,868 benign applications and 4,868 malicious applications. The experimental results show that our malware detection approach can achieve over 95.8% in accuracy, precision, recall, and F-Score. Compared with two state-of-the-art approaches, we can achieve a better malware detection rate of 97.88%.

AB - With the widespread use of Android applications in security-sensitive scenarios, more and more Android malware has been discovered. Existing work on malware detection fail to automatically learn effective feature interactions, which are, however, the key to the success of many prediction models. In order to detect malware efficiently and accurately, in this paper, we propose Multilevel Permission Extraction, an approach to automatically identify permission interactions that are effective in distinguishing between malicious and benign applications. We then utilize the extracted information to classify malicious and benign applications by machine learning based classification algorithms. We evaluate our approach in a large data set consisting of 4,868 benign applications and 4,868 malicious applications. The experimental results show that our malware detection approach can achieve over 95.8% in accuracy, precision, recall, and F-Score. Compared with two state-of-the-art approaches, we can achieve a better malware detection rate of 97.88%.

UR - http://www.scopus.com/inward/record.url?scp=85074162048&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85074162048&partnerID=8YFLogxK

U2 - 10.1109/CITS.2019.8862060

DO - 10.1109/CITS.2019.8862060

M3 - Conference contribution

AN - SCOPUS:85074162048

T3 - CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems

BT - CITS 2019 - Proceeding of the 2019 International Conference on Computer, Information and Telecommunication Systems

A2 - Obaidat, Mohammad S.

A2 - Mi, Zhenqiang

A2 - Hsiao, Kuei-Fang

A2 - Nicopolitidis, Petros

A2 - Cascado-Caballero, Daniel

PB - Institute of Electrical and Electronics Engineers Inc.

ER -