research-article

Android malware detection using convolutional neural networks and data section images

Authors:

Youngsup HwangAuthors Info & Claims

RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems

Pages 149 - 153

https://doi.org/10.1145/3264746.3264780

Published: 09 October 2018 Publication History

Abstract

The paper proposes a new technique to detect Android malware effectively based on converting malware binaries into images and applying machine learning techniques on those images. Existing research converts the whole executable files (e.g., DEX files in Android application package) of target apps into images and uses them for machine learning. However, the entire DEX file (consisting of header section, identifier section, data section, optional link data area, etc.) might contain noisy information for malware detection. In this paper, we convert only data sections of DEX files into grayscale images and apply machine learning on the images with Convolutional Neural Networks (CNN). By using only the data sections for 5,377 malicious and 6,249 benign apps, our technique reduces the storage capacity by 17.5% on average compared to using the whole DEX files. We apply two CNN models, Inception-v3 and Inception-ResNet-v2, which are known to be efficient in image processing, and examine the effectiveness of our technique in terms of accuracy. Experiment results show that the proposed technique achieves better accuracy with smaller storage capacity than the approach using the whole DEX files. Inception-ResNet-v2 with the stochastic gradient descent (SGD) optimization algorithm reaches 98.02% accuracy.

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Cited By

Kiraz ÖDoğru İ(2024)Visualising Static Features and Classifying Android Malware Using a Convolutional Neural Network ApproachApplied Sciences10.3390/app1411477214:11(4772)Online publication date: 31-May-2024
https://doi.org/10.3390/app14114772
Aldini APetrelli T(2024)Image-based detection and classification of Android malware through CNN modelsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670441(1-11)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670441
Khalifa MElsayed AHussien AHussainy A(2024)Android Malware Detection and Prevention Based on Deep Learning and Tweets Analysis2024 6th International Conference on Computing and Informatics (ICCI)10.1109/ICCI61671.2024.10485022(153-157)Online publication date: 6-Mar-2024
https://doi.org/10.1109/ICCI61671.2024.10485022
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Index Terms

Android malware detection using convolutional neural networks and data section images
1. Security and privacy
  1. Software and application security

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cover image ACM Conferences

RACS '18: Proceedings of the 2018 Conference on Research in Adaptive and Convergent Systems

October 2018

355 pages

ISBN:9781450358859

DOI:10.1145/3264746

Conference Chair:
Chih-Cheng Hung
Kennesaw State University
,
General Chair:
Lamjed Ben Said
University of Tunis, Tunisia

Copyright © 2018 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGAPP: ACM Special Interest Group on Applied Computing

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KISM: Korean Institute of Smart Media

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New York, NY, United States

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Published: 09 October 2018

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RACS '18: International Conference on Research in Adaptive and Convergent Systems

October 9 - 12, 2018

Hawaii, Honolulu

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Overall Acceptance Rate 393 of 1,581 submissions, 25%

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Cited By

Kiraz ÖDoğru İ(2024)Visualising Static Features and Classifying Android Malware Using a Convolutional Neural Network ApproachApplied Sciences10.3390/app1411477214:11(4772)Online publication date: 31-May-2024
https://doi.org/10.3390/app14114772
Aldini APetrelli T(2024)Image-based detection and classification of Android malware through CNN modelsProceedings of the 19th International Conference on Availability, Reliability and Security10.1145/3664476.3670441(1-11)Online publication date: 30-Jul-2024
https://dl.acm.org/doi/10.1145/3664476.3670441
Khalifa MElsayed AHussien AHussainy A(2024)Android Malware Detection and Prevention Based on Deep Learning and Tweets Analysis2024 6th International Conference on Computing and Informatics (ICCI)10.1109/ICCI61671.2024.10485022(153-157)Online publication date: 6-Mar-2024
https://doi.org/10.1109/ICCI61671.2024.10485022
Haidros Rahima Manzil HManohar Naik S(2024)Detection approaches for android malware: Taxonomy and review analysisExpert Systems with Applications10.1016/j.eswa.2023.122255238(122255)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.122255
Shu LDong SSu HHuang J(2023)Android Malware Detection Methods Based on Convolutional Neural Network: A SurveyIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32818337:5(1330-1350)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3281833
Lan QDu YGao CZheng H(2023)Status and Outlook of Image-based Malware Detection Technology2023 3rd International Symposium on Computer Technology and Information Science (ISCTIS)10.1109/ISCTIS58954.2023.10213085(598-603)Online publication date: 7-Jul-2023
https://doi.org/10.1109/ISCTIS58954.2023.10213085
Sharma OSharma AKalia A(2023)Windows Malware Hunting with InceptionResNetv2 Assisted Malware Visualization ApproachProceedings of International Conference on Computational Intelligence and Data Engineering10.1007/978-981-99-0609-3_12(171-188)Online publication date: 18-Jun-2023
https://doi.org/10.1007/978-981-99-0609-3_12
Mazaed Alotaibi FFawad (2022)A Multifaceted Deep Generative Adversarial Networks Model for Mobile Malware DetectionApplied Sciences10.3390/app1219940312:19(9403)Online publication date: 20-Sep-2022
https://doi.org/10.3390/app12199403
Naeem HAlshammari BUllah F(2022)Explainable Artificial Intelligence-Based IoT Device Malware Detection Mechanism Using Image Visualization and Fine-Tuned CNN-Based Transfer Learning ModelComputational Intelligence and Neuroscience10.1155/2022/76719672022Online publication date: 15-Jul-2022
https://dl.acm.org/doi/10.1155/2022/7671967
Yeboah PBaz Musah H(2022)NLP Technique for Malware Detection Using 1D CNN Fusion ModelSecurity and Communication Networks10.1155/2022/29572032022(1-9)Online publication date: 10-Jun-2022
https://doi.org/10.1155/2022/2957203
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