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Adversarial malware sample generation method based on the prototype of deep learning detector

Yanchen Qiao Orcid Logo, Weizhe Zhang, Zhicheng Tian, Laurence T. Yang, Yang Liu Orcid Logo, Mamoun Alazab

Computers & Security, Volume: 119, Start page: 102762

Swansea University Author: Yang Liu Orcid Logo

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DOI (Published version): 10.1016/j.cose.2022.102762

Published in: Computers & Security
ISSN: 0167-4048
Published: Elsevier BV 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa67396
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first_indexed 2024-09-20T12:53:19Z
last_indexed 2024-09-20T12:53:19Z
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spelling v2 67396 2024-08-15 Adversarial malware sample generation method based on the prototype of deep learning detector ba37dab58c9093dc63c79001565b75d4 0000-0003-2486-5765 Yang Liu Yang Liu true false 2024-08-15 MACS Journal Article Computers &amp; Security 119 102762 Elsevier BV 0167-4048 Adversarial example; Deep learning; Model interpretability; Malware detection; Prototype 1 8 2022 2022-08-01 10.1016/j.cose.2022.102762 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University This work is supported by the Major Key Project of PCL (No. PCL2021A02), the Key-Area Research and Development Program of Guangdong Province (No. 2020B0101360001), and the National Natural Science Foundation of China (No. 62102202). 2024-09-20T13:53:20.7767401 2024-08-15T17:04:19.1594040 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Yanchen Qiao 0000-0002-5009-3095 1 Weizhe Zhang 2 Zhicheng Tian 3 Laurence T. Yang 4 Yang Liu 0000-0003-2486-5765 5 Mamoun Alazab 6
title Adversarial malware sample generation method based on the prototype of deep learning detector
spellingShingle Adversarial malware sample generation method based on the prototype of deep learning detector
Yang Liu
title_short Adversarial malware sample generation method based on the prototype of deep learning detector
title_full Adversarial malware sample generation method based on the prototype of deep learning detector
title_fullStr Adversarial malware sample generation method based on the prototype of deep learning detector
title_full_unstemmed Adversarial malware sample generation method based on the prototype of deep learning detector
title_sort Adversarial malware sample generation method based on the prototype of deep learning detector
author_id_str_mv ba37dab58c9093dc63c79001565b75d4
author_id_fullname_str_mv ba37dab58c9093dc63c79001565b75d4_***_Yang Liu
author Yang Liu
author2 Yanchen Qiao
Weizhe Zhang
Zhicheng Tian
Laurence T. Yang
Yang Liu
Mamoun Alazab
format Journal article
container_title Computers &amp; Security
container_volume 119
container_start_page 102762
publishDate 2022
institution Swansea University
issn 0167-4048
doi_str_mv 10.1016/j.cose.2022.102762
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
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 0
active_str 0
published_date 2022-08-01T13:53:19Z
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score 11.031947

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