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Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis

Farhan Ullah Orcid Logo, Cheng Cheng Orcid Logo, Leonardo Mostarda Orcid Logo, Sohail Jabbar

Journal of Database Management, Volume: 34, Issue: 2, Pages: 1 - 26

Swansea University Author: Cheng Cheng Orcid Logo

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DOI (Published version): 10.4018/jdm.318414

Abstract

Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-bas...

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Published in: Journal of Database Management
ISSN: 1063-8016 1533-8010
Published: IGI Global 2023
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URI: https://cronfa.swan.ac.uk/Record/cronfa65950
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first_indexed 2024-05-29T14:07:26Z
last_indexed 2024-05-29T14:07:26Z
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spelling v2 65950 2024-04-03 Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis 11ddf61c123b99e59b00fa1479367582 0000-0003-0371-9646 Cheng Cheng Cheng Cheng true false 2024-04-03 MACS Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection. Journal Article Journal of Database Management 34 2 1 26 IGI Global 1063-8016 1533-8010 Android, Deep Learning, HTTP flow analysis, Internet of Things, LSA, Malware detection, N-gram, TextSemantics 16 2 2023 2023-02-16 10.4018/jdm.318414 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee 2024-05-29T15:09:22.8903250 2024-04-03T17:43:46.9964377 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Farhan Ullah 0000-0002-1030-1275 1 Cheng Cheng 0000-0003-0371-9646 2 Leonardo Mostarda 0000-0001-8852-8317 3 Sohail Jabbar 4 65950__30482__dcfeaba174fd43579d241ef1c81acd1e.pdf 65950.VoR.pdf 2024-05-29T15:07:56.4384519 Output 1512200 application/pdf Version of Record true This article published as an Open Access article distributed under the terms of the Creative Commons Attribution License. true eng http://creativecommons.org/licenses/by/4.0/
title Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
spellingShingle Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
Cheng Cheng
title_short Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
title_full Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
title_fullStr Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
title_full_unstemmed Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
title_sort Android-IoT Malware Classification and Detection Approach Using Deep URL Features Analysis
author_id_str_mv 11ddf61c123b99e59b00fa1479367582
author_id_fullname_str_mv 11ddf61c123b99e59b00fa1479367582_***_Cheng Cheng
author Cheng Cheng
author2 Farhan Ullah
Cheng Cheng
Leonardo Mostarda
Sohail Jabbar
format Journal article
container_title Journal of Database Management
container_volume 34
container_issue 2
container_start_page 1
publishDate 2023
institution Swansea University
issn 1063-8016
1533-8010
doi_str_mv 10.4018/jdm.318414
publisher IGI Global
college_str Faculty of Science and Engineering
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hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description Currently, malware attacks pose a high risk to compromise the security of Android-IoT apps. These threats have the potential to steal critical information, causing economic, social, and financial harm. Because of their constant availability on the network, Android apps are easily attacked by URL-based traffic. In this paper, an Android malware classification and detection approach using deep and broad URL feature mining is proposed. This study entails the development of a novel traffic data preprocessing and transformation method that can detect malicious apps using network traffic analysis. The encrypted URL-based traffic is mined to decrypt the transmitted data. To extract the sequenced features, the N-gram analysis method is used, and afterward, the singular value decomposition (SVD) method is utilized to reduce the features while preserving the actual semantics. The latent features are extracted using the latent semantic analysis tool. Finally, CNN-LSTM, a multi-view deep learning approach, is designed for effective malware classification and detection.
published_date 2023-02-16T15:09:21Z
_version_ 1800396353671528448
score 11.035786

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