Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes

Hamad H. Alsheraifi; Hussain M. Sajwani; Saeed M. Aljaberi; Abdelrahman A. Alblooshi; Ali H. Alhashmi; Saoud A. Sharif; Ernesto Damiani

doi:10.1109/BigData55660.2022.10021022

Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes

Hamad H. Alsheraifi, Hussain M. Sajwani, Saeed M. Aljaberi, Abdelrahman A. Alblooshi, Ali H. Alhashmi, Saoud A. Sharif, Ernesto Damiani

Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.

Original language	British English
Title of host publication	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
Editors	Shusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	6516-6521
Number of pages	6
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10021022
State	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Android Malware Detection
Deep Learning
Generative Adversarial Networks
System Calls

Access to Document

10.1109/BigData55660.2022.10021022

Cite this

Alsheraifi, H. H., Sajwani, H. M., Aljaberi, S. M., Alblooshi, A. A., Alhashmi, A. H., Sharif, S. A., & Damiani, E. (2022). Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022 (pp. 6516-6521). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BigData55660.2022.10021022

Alsheraifi, Hamad H. ; Sajwani, Hussain M. ; Aljaberi, Saeed M. et al. / Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 6516-6521 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inproceedings{35b19a204a55486d8c242e86cb9517ea,

title = "Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes",

abstract = "Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.",

keywords = "Android Malware Detection, Deep Learning, Generative Adversarial Networks, System Calls",

author = "Alsheraifi, {Hamad H.} and Sajwani, {Hussain M.} and Aljaberi, {Saeed M.} and Alblooshi, {Abdelrahman A.} and Alhashmi, {Ali H.} and Sharif, {Saoud A.} and Ernesto Damiani",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2022",

doi = "10.1109/BigData55660.2022.10021022",

language = "British English",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "6516--6521",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

address = "United States",

}

Alsheraifi, HH, Sajwani, HM, Aljaberi, SM, Alblooshi, AA, Alhashmi, AH, Sharif, SA & Damiani, E 2022, Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, Institute of Electrical and Electronics Engineers Inc., pp. 6516-6521, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. https://doi.org/10.1109/BigData55660.2022.10021022

Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes. / Alsheraifi, Hamad H.; Sajwani, Hussain M.; Aljaberi, Saeed M. et al.
Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Institute of Electrical and Electronics Engineers Inc., 2022. p. 6516-6521 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes

AU - Alsheraifi, Hamad H.

AU - Sajwani, Hussain M.

AU - Aljaberi, Saeed M.

AU - Alblooshi, Abdelrahman A.

AU - Alhashmi, Ali H.

AU - Sharif, Saoud A.

AU - Damiani, Ernesto

PY - 2022

Y1 - 2022

N2 - Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.

AB - Gathering the training malware traces is restive and can be a nuisance depending on the type of malware, such as behavioral polymorphism. Generative Adversarial Networks (GANs) are well suited for these issues because they can generate synthetic data that mimic actual data. This treatise sheds detailed and thorough insights into the GAN model implemented to generate a proper training mechanism for binary classification. This paper tested tabular-based and pictorial-based models in multiple trials to determine the better one for classification. Furthermore, multiple ML-based classification techniques, such as ensemble learning, Support Vector Machines (SVMs), and Linear Regression, were tested and recorded on tabular and pictorial GANs. Tabular-wise, the RMSE data collected for Random Forest Tree with the Vanilla LeakyReLU-based GAN provided the optimal classification results. Feature interactions and a biological-inspired activation function were considered for optimizing the model. However, they were only additional tests that were not considered part of the leading paper, as testing quantity was insufficient for definitive evidence of optimization.

KW - Android Malware Detection

KW - Deep Learning

KW - Generative Adversarial Networks

KW - System Calls

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U2 - 10.1109/BigData55660.2022.10021022

DO - 10.1109/BigData55660.2022.10021022

M3 - Conference contribution

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T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 6516

EP - 6521

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - Institute of Electrical and Electronics Engineers Inc.

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Alsheraifi HH, Sajwani HM, Aljaberi SM, Alblooshi AA, Alhashmi AH, Sharif SA et al. Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 6516-6521. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10021022

Using Generative Adversarial Networks to Simulate System Calls of Malicious Android Processes

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