{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,3]],"date-time":"2026-01-03T15:09:32Z","timestamp":1767452972577,"version":"3.37.3"},"reference-count":8,"publisher":"Wiley","license":[{"start":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T00:00:00Z","timestamp":1554076800000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2016M590234","LG201611","18-013-0-32","20180551066"],"award-info":[{"award-number":["2016M590234","LG201611","18-013-0-32","20180551066"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]},{"name":"General Project of Liaoning Provincial Department of Education","award":["2016M590234","LG201611","18-013-0-32","20180551066"],"award-info":[{"award-number":["2016M590234","LG201611","18-013-0-32","20180551066"]}]},{"name":"Postdoctoral fund of Shenyang Ligong University, Project of Applied Basic Research of Shenyang","award":["2016M590234","LG201611","18-013-0-32","20180551066"],"award-info":[{"award-number":["2016M590234","LG201611","18-013-0-32","20180551066"]}]},{"name":"Liaoning Nature Foundation","award":["2016M590234","LG201611","18-013-0-32","20180551066"],"award-info":[{"award-number":["2016M590234","LG201611","18-013-0-32","20180551066"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Security and Communication Networks"],"published-print":{"date-parts":[[2019,4,1]]},"abstract":"<jats:p>The increasing sophistication of malware variants such as encryption, polymorphism, and obfuscation calls for the new detection and classification technology. In this paper, MalDeep, a novel malware classification framework of deep learning based on texture visualization, is proposed against malicious variants. Through code mapping, texture partitioning, and texture extracting, we can study malware classification in a new feature space of image texture representation without decryption and disassembly. Furthermore, we built a malware classifier on convolutional neural network with two convolutional layers, two downsampling layers, and many full connection layers. We adopt the dataset, from Microsoft Malware Classification Challenge including 9 categories of malware families and 10868 variant samples, to train the model. The experiment results show that the established MalDeep has a higher accuracy rate for malware classification. In particular, for some backdoor families, the classification accuracy of the model reaches over 99%. Moreover, compared with other main antivirus software, MalDeep also outperforms others in the average accuracy for the variants from different families.<\/jats:p>","DOI":"10.1155\/2019\/4895984","type":"journal-article","created":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T19:34:55Z","timestamp":1554147295000},"page":"1-11","source":"Crossref","is-referenced-by-count":20,"title":["MalDeep: A Deep Learning Classification Framework against Malware Variants Based on Texture Visualization"],"prefix":"10.1155","volume":"2019","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2627-8276","authenticated-orcid":true,"given":"Yuntao","family":"Zhao","sequence":"first","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"},{"name":"College of Information Science and Engineering, Northeastern University, Shenyang 110819, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chunyu","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bo","family":"Bo","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8632-7834","authenticated-orcid":true,"given":"Yongxin","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Information Science and Engineering, Shenyang Ligong University, Shenyang 110159, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"3","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-006-0009-x"},{"key":"4","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-006-0026-9"},{"key":"6","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2017.23353"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1007\/s11416-008-0082-4"},{"first-page":"178","volume-title":"Automated classification and analysis of internet malware","year":"2007","key":"7"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.1145\/2019618.2019622"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.1002\/9780470148150"},{"issue":"8","key":"17","first-page":"126","volume":"35","year":"2014","journal-title":"Journal on Communications"}],"container-title":["Security and Communication Networks"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2019\/4895984.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2019\/4895984.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/scn\/2019\/4895984.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2019,4,1]],"date-time":"2019-04-01T19:34:59Z","timestamp":1554147299000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/scn\/2019\/4895984\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,4,1]]},"references-count":8,"alternative-id":["4895984","4895984"],"URL":"https:\/\/doi.org\/10.1155\/2019\/4895984","relation":{},"ISSN":["1939-0114","1939-0122"],"issn-type":[{"type":"print","value":"1939-0114"},{"type":"electronic","value":"1939-0122"}],"subject":[],"published":{"date-parts":[[2019,4,1]]}}}