{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,2,21]],"date-time":"2025-02-21T20:18:47Z","timestamp":1740169127937,"version":"3.37.3"},"reference-count":45,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2019,1,1]],"date-time":"2019-01-01T00:00:00Z","timestamp":1546300800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/OAPA.html"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2019]]},"DOI":"10.1109\/access.2019.2915681","type":"journal-article","created":{"date-parts":[[2019,5,8]],"date-time":"2019-05-08T16:04:36Z","timestamp":1557331476000},"page":"68786-68798","source":"Crossref","is-referenced-by-count":3,"title":["NeuralTaint: A Key Segment Marking Tool Based on Neural Network"],"prefix":"10.1109","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7730-9212","authenticated-orcid":false,"given":"Xing","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2129-9665","authenticated-orcid":false,"given":"Runhao","family":"Li","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5838-5826","authenticated-orcid":false,"given":"Jing","family":"Lei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chaojing","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"doi-asserted-by":"publisher","key":"ref39","DOI":"10.1109\/SP.2018.00046"},{"doi-asserted-by":"publisher","key":"ref38","DOI":"10.14722\/ndss.2017.23404"},{"key":"ref33","first-page":"99","article-title":"Neural nets can learn function type signatures from binaries","author":"chua","year":"2017","journal-title":"Proc 26th USENIX Security Symp (USENIX Security)"},{"key":"ref32","first-page":"611","article-title":"Recognizing functions in binaries with neural networks","author":"shin","year":"2015","journal-title":"Proc 24th USENIX Secur Symp (USENIX Security)"},{"doi-asserted-by":"publisher","key":"ref31","DOI":"10.1145\/1961295.1950396"},{"doi-asserted-by":"publisher","key":"ref30","DOI":"10.1007\/978-3-540-89862-7_1"},{"key":"ref37","doi-asserted-by":"crossref","first-page":"259","DOI":"10.1145\/2666356.2594299","article-title":"Flowdroid: Precise context, flow, field, object-sensitive and lifecycle-aware taint analysis for Android apps","volume":"49","author":"arzt","year":"2014","journal-title":"ACM SIGPLAN Notices"},{"key":"ref36","first-page":"49","article-title":"Dowser: A guided fuzzer to find buffer overflow vulnerabilities","author":"haller","year":"2013","journal-title":"Proc 22nd USENIX Secur Symp"},{"doi-asserted-by":"publisher","key":"ref35","DOI":"10.1109\/SP.2010.37"},{"year":"2018","author":"she","journal-title":"NEUZZ Efficient fuzzing with neural program smoothing","key":"ref34"},{"key":"ref10","first-page":"2","article-title":"Applied logistic regression","volume":"34","author":"cucchiara","year":"2012","journal-title":"Technometrics"},{"doi-asserted-by":"publisher","key":"ref40","DOI":"10.1145\/2843859.2843867"},{"doi-asserted-by":"publisher","key":"ref11","DOI":"10.1093\/esr\/jcp006"},{"doi-asserted-by":"publisher","key":"ref12","DOI":"10.1109\/72.97934"},{"doi-asserted-by":"publisher","key":"ref13","DOI":"10.1016\/S0893-6080(09)80013-0"},{"doi-asserted-by":"publisher","key":"ref14","DOI":"10.1109\/TASSP.1985.1164550"},{"doi-asserted-by":"publisher","key":"ref15","DOI":"10.1109\/ASYNC.1998.666496"},{"key":"ref16","first-page":"1","article-title":"Large-margin softmax loss for convolutional neural networks","author":"liu","year":"2016","journal-title":"Proc Int Conf Mach Learn"},{"doi-asserted-by":"publisher","key":"ref17","DOI":"10.1613\/jair.251"},{"doi-asserted-by":"publisher","key":"ref18","DOI":"10.1007\/978-3-319-70096-0_43"},{"doi-asserted-by":"publisher","key":"ref19","DOI":"10.1016\/j.patcog.2005.03.024"},{"year":"2018","journal-title":"American Fuzzy Lop","key":"ref28"},{"doi-asserted-by":"publisher","key":"ref4","DOI":"10.1109\/DSN.2009.5270315"},{"doi-asserted-by":"publisher","key":"ref27","DOI":"10.1109\/ICIP.2013.6738831"},{"doi-asserted-by":"publisher","key":"ref3","DOI":"10.1145\/1368088.1368127"},{"doi-asserted-by":"publisher","key":"ref6","DOI":"10.1109\/ISSRE.2010.38"},{"year":"2018","journal-title":"PinTool","key":"ref29"},{"doi-asserted-by":"publisher","key":"ref5","DOI":"10.1109\/CECNet.2013.6703400"},{"key":"ref8","first-page":"359","article-title":"Smooth regression analysis","volume":"26","author":"watson","year":"1964","journal-title":"Sankhy The Indian J Statist"},{"doi-asserted-by":"publisher","key":"ref7","DOI":"10.1561\/2000000039"},{"doi-asserted-by":"publisher","key":"ref2","DOI":"10.1145\/2393596.2393650"},{"key":"ref9","first-page":"219","article-title":"Nonlinear regression analysis and its applications","volume":"32","author":"bates","year":"1988","journal-title":"Technometrics"},{"doi-asserted-by":"publisher","key":"ref1","DOI":"10.1109\/SP.2010.26"},{"year":"2017","author":"li","journal-title":"Convergence analysis of two-layer neural networks with relu activation","key":"ref20"},{"key":"ref45","first-page":"745","article-title":"QSYM: A practical concolic execution engine tailored for hybrid fuzzing","author":"yun","year":"2018","journal-title":"Proc 7th USENIX Secur Symp (USENIX Secur )"},{"doi-asserted-by":"publisher","key":"ref22","DOI":"10.1016\/0169-7439(95)80039-C"},{"year":"2019","author":"eger","journal-title":"Is it time to swish? Comparing deep learning activation functions across NLP tasks","key":"ref21"},{"year":"2014","author":"lecomte","journal-title":"Élaboration d’une représentation intermédiaire pour l’exécution concolique et le marquage de données sous windows","key":"ref42"},{"doi-asserted-by":"publisher","key":"ref24","DOI":"10.1109\/42.476112"},{"key":"ref41","first-page":"1","article-title":"QEMU, a fast and portable dynamic translator","volume":"41","author":"bellard","year":"2005","journal-title":"Proc USENIX Annu Tech Conf"},{"key":"ref23","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.cosrev.2009.03.005","article-title":"Reservoir computing approaches to recurrent neural network training","volume":"3","author":"luko\u0161evi?ius","year":"2009","journal-title":"Comput Sci Rev"},{"doi-asserted-by":"publisher","key":"ref44","DOI":"10.14722\/ndss.2016.23368"},{"doi-asserted-by":"publisher","key":"ref26","DOI":"10.3115\/v1\/P14-1062"},{"year":"2012","author":"pak","journal-title":"Hybrid Fuzz Testing Discovering Software Bugs Via Fuzzing and Symbolic Execution","key":"ref43"},{"key":"ref25","first-page":"1","article-title":"Recurrent neural network based language model","author":"mikolov","year":"2010","journal-title":"Proc 11th Annu Conf Int Speech Commun Assoc"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/8600701\/08709689.pdf?arnumber=8709689","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,12]],"date-time":"2022-01-12T11:54:28Z","timestamp":1641988468000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/8709689\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019]]},"references-count":45,"URL":"https:\/\/doi.org\/10.1109\/access.2019.2915681","relation":{},"ISSN":["2169-3536"],"issn-type":[{"type":"electronic","value":"2169-3536"}],"subject":[],"published":{"date-parts":[[2019]]}}}