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Dynamic android malware category classification using semi-supervised deep learning

Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine lear...

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Main Authors: Mahdavifar, Samaneh, Kadir, Andi Fitriah Abdul, Fatemi, Rasool, Alhadidi, Dima, Ghorbani, Ali A
Format: Conference or Workshop Item
Language:English
English
English
Published: IEEE 2020
Subjects:
T Technology (General)
Online Access:http://irep.iium.edu.my/85914/3/DASC_best_student%20paper.pdf
http://irep.iium.edu.my/85914/9/85914%20Dynamic%20Android%20Malware%20Category%20Classification.pdf
http://irep.iium.edu.my/85914/15/85914_Dynamic%20Android%20Malware%20Category%20Classification%20using%20Semi-Supervised%20SCOPUS.pdf
http://irep.iium.edu.my/85914/
https://ieeexplore.ieee.org/document/9251198
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spelling my.iium.irep.859142021-01-17T05:28:17Z http://irep.iium.edu.my/85914/ Dynamic android malware category classification using semi-supervised deep learning Mahdavifar, Samaneh Kadir, Andi Fitriah Abdul Fatemi, Rasool Alhadidi, Dima Ghorbani, Ali A T Technology (General) Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. We use dynamic analysis to craft dynamic behavior profiles as feature vectors. Furthermore, we develop a new dataset, namely CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories: Adware, Banking, SMS, Riskware, and Benign. Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. The experimental results show that the model can classify Android apps with respect to malware category with F 1 -Score of 97.84 percent and a false positive rate of 2.76 percent, considerably higher than LP. These results demonstrate the robustness of our model despite the small number of labeled instances. IEEE 2020-11-11 Conference or Workshop Item PeerReviewed application/pdf en http://irep.iium.edu.my/85914/3/DASC_best_student%20paper.pdf application/pdf en http://irep.iium.edu.my/85914/9/85914%20Dynamic%20Android%20Malware%20Category%20Classification.pdf application/pdf en http://irep.iium.edu.my/85914/15/85914_Dynamic%20Android%20Malware%20Category%20Classification%20using%20Semi-Supervised%20SCOPUS.pdf Mahdavifar, Samaneh and Kadir, Andi Fitriah Abdul and Fatemi, Rasool and Alhadidi, Dima and Ghorbani, Ali A (2020) Dynamic android malware category classification using semi-supervised deep learning. In: 2020 IEEE International Conference on Dependable, Autonomic and Secure Computing (DASC 2020, 17th-22nd August 2020, Calgary, AB, Canada. https://ieeexplore.ieee.org/document/9251198 10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00094
institution Universiti Islam Antarabangsa Malaysia
building IIUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider International Islamic University Malaysia
content_source IIUM Repository (IREP)
url_provider http://irep.iium.edu.my/
language English
English
English
topic T Technology (General)
spellingShingle T Technology (General)
Mahdavifar, Samaneh
Kadir, Andi Fitriah Abdul
Fatemi, Rasool
Alhadidi, Dima
Ghorbani, Ali A
Dynamic android malware category classification using semi-supervised deep learning
description Due to the significant threat of Android mobile malware, its detection has become increasingly important. Despite the academic and industrial attempts, devising a robust and efficient solution for Android malware detection and category classification is still an open problem. Supervised machine learning has been used to solve this issue. However, it is far to be perfect because it requires a significant amount of malicious and benign code to be identified and labeled beforehand. Since labeled data is expensive and difficult to get while unlabeled data is abundant and cheap in this context, we resort to a semi-supervised learning technique for deep neural networks, namely pseudo-label, which we train using a set of labeled and unlabeled instances. We use dynamic analysis to craft dynamic behavior profiles as feature vectors. Furthermore, we develop a new dataset, namely CICMalDroid2020, which includes 17,341 most recent samples of five different Android apps categories: Adware, Banking, SMS, Riskware, and Benign. Our offered dataset comprises the most complete captured static and dynamic features among publicly available datasets. We evaluate our proposed model on CICMalDroid2020 and conduct a comparison with Label Propagation (LP), a well-known semi-supervised machine learning technique, and other common machine learning algorithms. The experimental results show that the model can classify Android apps with respect to malware category with F 1 -Score of 97.84 percent and a false positive rate of 2.76 percent, considerably higher than LP. These results demonstrate the robustness of our model despite the small number of labeled instances.
format Conference or Workshop Item
author Mahdavifar, Samaneh
Kadir, Andi Fitriah Abdul
Fatemi, Rasool
Alhadidi, Dima
Ghorbani, Ali A
author_facet Mahdavifar, Samaneh
Kadir, Andi Fitriah Abdul
Fatemi, Rasool
Alhadidi, Dima
Ghorbani, Ali A
author_sort Mahdavifar, Samaneh
title Dynamic android malware category classification using semi-supervised deep learning
title_short Dynamic android malware category classification using semi-supervised deep learning
title_full Dynamic android malware category classification using semi-supervised deep learning
title_fullStr Dynamic android malware category classification using semi-supervised deep learning
title_full_unstemmed Dynamic android malware category classification using semi-supervised deep learning
title_sort dynamic android malware category classification using semi-supervised deep learning
publisher IEEE
publishDate 2020
url http://irep.iium.edu.my/85914/3/DASC_best_student%20paper.pdf
http://irep.iium.edu.my/85914/9/85914%20Dynamic%20Android%20Malware%20Category%20Classification.pdf
http://irep.iium.edu.my/85914/15/85914_Dynamic%20Android%20Malware%20Category%20Classification%20using%20Semi-Supervised%20SCOPUS.pdf
http://irep.iium.edu.my/85914/
https://ieeexplore.ieee.org/document/9251198
_version_ 1690370748020948992
score 13.211869

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