{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T09:51:24Z","timestamp":1785577884043,"version":"3.56.0"},"reference-count":71,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"1","license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["201706840123"],"award-info":[{"award-number":["201706840123"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1804604"],"award-info":[{"award-number":["2020YFB1804604"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1804600"],"award-info":[{"award-number":["2020YFB1804600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2020YFB1805503"],"award-info":[{"award-number":["2020YFB1805503"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Industry and Information Technology of China"},{"name":"2018 Jiangsu Province Major Technical Research Project Information Security Simulation System"},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30918012204"],"award-info":[{"award-number":["30918012204"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["30920041112"],"award-info":[{"award-number":["30920041112"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ministry of Industry and Information Technology of China"},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["#1814825"],"award-info":[{"award-number":["#1814825"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Trans. Netw. Sci. Eng."],"published-print":{"date-parts":[[2021,1,1]]},"DOI":"10.1109\/tnse.2021.3051354","type":"journal-article","created":{"date-parts":[[2021,1,14]],"date-time":"2021-01-14T22:41:48Z","timestamp":1610664108000},"page":"736-750","source":"Crossref","is-referenced-by-count":60,"title":["A Framework for Enhancing Deep Neural Networks Against Adversarial Malware"],"prefix":"10.1109","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3456-902X","authenticated-orcid":false,"given":"Deqiang","family":"Li","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0998-1517","authenticated-orcid":false,"given":"Qianmu","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6038-2173","authenticated-orcid":false,"given":"Yanfang","family":"Ye","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8034-0942","authenticated-orcid":false,"given":"Shouhuai","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref71","article-title":"Adversarial training and provable defenses: Bridging the gap","author":"balunovic","year":"0","journal-title":"Proc Int Conf Learn Representations"},{"key":"ref70","doi-asserted-by":"publisher","DOI":"10.1186\/s13638-020-01782-6"},{"key":"ref39","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3133978"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1145\/3134600.3134636"},{"key":"ref33","doi-asserted-by":"publisher","DOI":"10.1145\/3133956.3134057"},{"key":"ref32","first-page":"pp. 3371?3408","article-title":"Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion","volume":"11","author":"vincent","year":"2010","journal-title":"J Mach Learn Res"},{"key":"ref31","article-title":"Adversarial training methods for semi-supervised text classification","author":"miyato","year":"0"},{"key":"ref30","doi-asserted-by":"publisher","DOI":"10.1109\/ICDM.2015.84"},{"key":"ref37","article-title":"Generic black-box end-to-end attack against rnns and other calls based malware classifiers","author":"rosenberg","year":"0"},{"key":"ref36","first-page":"197","article-title":"Practical evasion of a learning-based classifier: A. case study,&#x201D; in security and privacy (SP)","author":"nedimrndic","year":"0","journal-title":"Proc IEEE Symp"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"ref34","article-title":"Aics 2019 Workshop Challenge Problem","author":"nazir","year":"0"},{"key":"ref60","article-title":"Androguard, ONLINE","author":"desnos","year":"0"},{"key":"ref62","doi-asserted-by":"publisher","DOI":"10.1145\/3005714"},{"key":"ref61","author":"revivo","year":"2017","journal-title":"Cuckoodroid"},{"key":"ref63","article-title":"Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples","author":"athalye","year":"0"},{"key":"ref28","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"0"},{"key":"ref64","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tram\u00e8r","year":"0"},{"key":"ref27","article-title":"Adversarial examples in the physical world","author":"kurakin","year":"0"},{"key":"ref65","doi-asserted-by":"publisher","DOI":"10.1145\/3052973.3053009"},{"key":"ref66","first-page":"pp. 1?5","article-title":"The truth of the f-measure","volume":"1","author":"sasaki","year":"2007","journal-title":"Teach Tutor Mater"},{"key":"ref29","doi-asserted-by":"publisher","DOI":"10.1109\/72.165600"},{"key":"ref67","doi-asserted-by":"publisher","DOI":"10.1109\/ISKE.2017.8258804"},{"key":"ref68","doi-asserted-by":"publisher","DOI":"10.1145\/3219819.3219862"},{"key":"ref69","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.105086"},{"key":"ref2","article-title":"Symantec, ONLINE","year":"2018"},{"key":"ref1","article-title":"Enhancing robustness of deep neural networks against adversarial malware samples: Principles, Framework, and aics'2019 challenge","author":"li","year":"2018"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2016.23078"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098158"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/MILCOM.2018.8599855"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/ASONAM.2018.8508284"},{"key":"ref23","article-title":"Hashtran-dnn: A. framework for enhancing robustness of deep neural networks against adversarial malware samples","author":"li","year":"0"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1109\/CNS.2014.6997494"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2018.23198"},{"key":"ref50","first-page":"pp. 832?844","article-title":"The random subspace method for constructing decision forests","volume":"20","author":"ho","year":"1998","journal-title":"IEEE Trans PAMI"},{"key":"ref51","first-page":"pp. 321?338","article-title":"Why do adversarial attacks transfer? Explaining transferability of evasion and poisoning attacks","author":"demontis","year":"0","journal-title":"Proc 28th USENIX Secur Symp"},{"key":"ref59","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-45719-2_11"},{"key":"ref58","article-title":"May) Virustotal","year":"2018"},{"key":"ref57","first-page":"pp. 321?338","author":"demontis","year":"2019","journal-title":"Proc 28th USENIX Secur Symp (USENIX Secur 19)"},{"key":"ref56","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2014.23247"},{"key":"ref55","doi-asserted-by":"publisher","DOI":"10.18653\/v1\/D15-1166"},{"key":"ref54","first-page":"pp. 3563?3593","article-title":"What regularized auto-encoders learn from the data-generating distribution","volume":"15","author":"alain","year":"2014","journal-title":"J Mach Learn Res"},{"key":"ref53","article-title":"Towards the first adversarially robust neural network model on MNIST","author":"schott","year":"2019"},{"key":"ref52","article-title":"Strength in numbers: Trading-off robustness and computation via adversarially-trained ensembles","author":"grefenstette","year":"2018"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TIFS.2020.3003571"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3374664.3375741"},{"key":"ref40","article-title":"Evading Machine Learning Malware Detection","author":"anderson","year":"2017"},{"key":"ref12","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"0"},{"key":"ref13","article-title":"Explaining and harnessing adversarial examples","author":"goodfellow","year":"0"},{"key":"ref14","article-title":"Adversarial examples-a complete characterisation of the phenomenon","author":"serban","year":"0"},{"key":"ref15","article-title":"SOK: Arms race in adversarial malware detection","author":"li","year":"0"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/TDSC.2017.2700270"},{"key":"ref17","article-title":"Utsa Wins Global Cyber Security Challenge, ONLINE","author":"nazir","year":"0"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0007-7"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12127-2_8"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/3073559"},{"key":"ref3","article-title":"Cisio, ONLINE","year":"2018"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/EISIC.2017.21"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-66399-9_4"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2018\/737"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1109\/SPW.2018.00020"},{"key":"ref49","doi-asserted-by":"publisher","DOI":"10.1201\/b12207"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.1109\/SP40000.2020.00073"},{"key":"ref46","article-title":"Adversarial training and robustness for multiple perturbations","author":"tram\u00e8r","year":"0"},{"key":"ref45","first-page":"pp. 1299?1316","article-title":"When does machine learning FAIL? generalized transferability for evasion and poisoning attacks","author":"suciu","year":"2018","journal-title":"Proc 27th USENIX Secur Symp (USENIX Security 18)"},{"key":"ref48","article-title":"Malware detection by eating a whole exe","author":"raff","year":"0"},{"key":"ref47","article-title":"Adam: A method for stochastic optimization","author":"kingma","year":"0"},{"key":"ref42","article-title":"Generating adversarial malware examples for black-box attacks based on gan","author":"hu","year":"0"},{"key":"ref41","doi-asserted-by":"publisher","DOI":"10.14722\/ndss.2016.23115"},{"key":"ref44","doi-asserted-by":"publisher","DOI":"10.1109\/EuroSP.2016.36"},{"key":"ref43","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"}],"container-title":["IEEE Transactions on Network Science and Engineering"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/6488902\/9380808\/9321695-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6488902\/9380808\/09321695.pdf?arnumber=9321695","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,5,10]],"date-time":"2022-05-10T14:53:36Z","timestamp":1652194416000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9321695\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,1]]},"references-count":71,"journal-issue":{"issue":"1"},"URL":"https:\/\/doi.org\/10.1109\/tnse.2021.3051354","relation":{},"ISSN":["2327-4697","2334-329X"],"issn-type":[{"value":"2327-4697","type":"electronic"},{"value":"2334-329X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,1]]}}}