{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,9,3]],"date-time":"2024-09-03T19:29:31Z","timestamp":1725391771053},"reference-count":40,"publisher":"IEEE","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2020,9,17]]},"DOI":"10.23919\/softcom50211.2020.9238337","type":"proceedings-article","created":{"date-parts":[[2020,10,28]],"date-time":"2020-10-28T20:07:18Z","timestamp":1603915638000},"page":"1-6","source":"Crossref","is-referenced-by-count":0,"title":["On Recent Security Issues in Machine Learning"],"prefix":"10.23919","author":[{"given":"Mohammed M.","family":"Alani","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref39","article-title":"Adversarial machine learning&#x2013; industry perspectives","author":"kumar","year":"2020","journal-title":"arXiv preprint arXiv 2002 05155"},{"key":"ref38","doi-asserted-by":"publisher","DOI":"10.1109\/TVCG.2019.2934631"},{"key":"ref33","article-title":"Ensemble adversarial training: Attacks and defenses","author":"tramer","year":"2017","journal-title":"arXiv preprint arXiv 1705 07204"},{"key":"ref32","doi-asserted-by":"publisher","DOI":"10.1145\/3319619.3326897"},{"key":"ref31","article-title":"Explaining vulnerabilities of deep learning to adversarial malware binaries","author":"demetrio","year":"2019","journal-title":"arXiv preprint arXiv 1901 04217"},{"journal-title":"Malware statistics &#x2013; you&#x2019;d better get your computer vaccinated","year":"2020","key":"ref30"},{"key":"ref37","article-title":"Symbolic execution for deep neural networks","author":"gopinath","year":"2018","journal-title":"arXiv preprint arXiv 1807 10439"},{"key":"ref36","doi-asserted-by":"publisher","DOI":"10.1016\/j.peva.2009.07.007"},{"key":"ref35","doi-asserted-by":"publisher","DOI":"10.1109\/ACSAC.2007.21"},{"key":"ref34","article-title":"Disparate vulnerability: On the unfairness of privacy attacks against machine learning","author":"yaghini","year":"2019","journal-title":"arXiv preprint arXiv 1906 03008"},{"key":"ref10","article-title":"Towards the science of security and privacy in machine learning","author":"papernot","year":"2016","journal-title":"arXiv preprint arXiv 1611 03814"},{"key":"ref40","doi-asserted-by":"publisher","DOI":"10.1109\/MC.2020.2980761"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.17648\/jisc.v7i1.76"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1145\/3336191.3371877"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1126\/science.aaw4399"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-98842-9_3"},{"key":"ref15","first-page":"1076","article-title":"A review on security attacks and protective strategies of machine learning","author":"meenakshi","year":"2019","journal-title":"International Conference on Emerging Current Trends in Computing and Expert Technology"},{"key":"ref16","article-title":"Attacking machine learning models as part of a cyber kill chain","author":"nguyen","year":"2018","journal-title":"arXiv preprint arXiv 1705 00564"},{"key":"ref17","article-title":"Intelligence-driven computer network defense informed by analysis of adversary campaigns and intrusion kill chains","author":"amin","year":"2011","journal-title":"Proc of CIW2011"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1145\/3287624.3288751"},{"key":"ref19","article-title":"Deeplocker&#x2013;concealing targeted attacks with ai locksmithing","author":"kirat","year":"2018","journal-title":"BlackHat USA"},{"key":"ref28","article-title":"Adversarial machine learning at scale","author":"kurakin","year":"2016","journal-title":"arXiv preprint arXiv 1611 01236"},{"journal-title":"oogle cloud ai platform","year":"2020","key":"ref4"},{"key":"ref27","article-title":"Towards deep learning models resistant to adversarial attacks","author":"madry","year":"2017","journal-title":"arXiv preprint arXiv 1706 06083"},{"journal-title":"IBM Watson Machine Learning","year":"2020","key":"ref3"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1145\/1128817.1128824"},{"key":"ref29","article-title":"Robust or private? adversarial training makes models more vulnerable to privacy attacks","author":"mejia","year":"2019","journal-title":"arXiv preprint arXiv 1906 03008"},{"journal-title":"Microsoft Azure Machine Learning Studio","year":"2020","key":"ref5"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-40994-3_25"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1007\/s10994-010-5188-5"},{"journal-title":"How Much Data Do We Create Every Day","year":"2020","key":"ref2"},{"key":"ref9","article-title":"Hacking smart machines with smarter ones: How to extract meaningful data from machine learning classifiers","author":"ateniese","year":"2013","journal-title":"arXiv preprint arXiv 1306 4447"},{"journal-title":"Machine Learning","year":"1997","author":"mitchell","key":"ref1"},{"key":"ref20","first-page":"111","article-title":"Timing attacks on machine learning: State of the art","author":"kianpour","year":"2019","journal-title":"Proceedings of SAI Intelligent Systems Conference"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/UCET.2019.8881843"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.49"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.1109\/SP.2017.41"},{"key":"ref23","article-title":"Dynamic backdoor attacks against machine learning models","author":"salem","year":"2020","journal-title":"arXiv preprint arXiv 2003 07516"},{"key":"ref26","article-title":"Intriguing properties of neural networks","author":"szegedy","year":"2013","journal-title":"arXiv preprint arXiv 1312 6199"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1109\/TSC.2019.2897554"}],"event":{"name":"2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)","start":{"date-parts":[[2020,9,17]]},"location":"Split, Croatia","end":{"date-parts":[[2020,9,19]]}},"container-title":["2020 International Conference on Software, Telecommunications and Computer Networks (SoftCOM)"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9238132\/9238148\/09238337.pdf?arnumber=9238337","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,12,20]],"date-time":"2021-12-20T23:46:29Z","timestamp":1640043989000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9238337\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,17]]},"references-count":40,"URL":"https:\/\/doi.org\/10.23919\/softcom50211.2020.9238337","relation":{},"subject":[],"published":{"date-parts":[[2020,9,17]]}}}