Open Access
ITM Web Conf.
Volume 47, 2022
2022 2nd International Conference on Computer, Communication, Control, Automation and Robotics (CCCAR2022)
Article Number 01005
Number of page(s) 5
Section Computer Science and System Design, Application
Published online 23 June 2022
  1. ong C, Ristenpart T, Shmatikov V. Machine learning models that remember too much. In: Proc. of the 2017 ACM SIGSAC Conf.on Computer and Communications Security. 2017. 587-601 [Google Scholar]
  2. Tramèr F, Zhang F, Juels A, et al. Stealing machine learning models via prediction apis. In: Proc. of the 25th {USENIX} Security Symp. ({USENIX} Security 2016). 2016. 601-618 [Google Scholar]
  3. ong C, Biggio B, Laskov P. Understanding the risk factors of learning in adversarial environments. AISec, 2011,11:87-92 [Google Scholar]
  4. P.Lamere,P.Kwork,W.Walker,E.Gouvea,R.Singh,B.Raj and P.Wolf,”Design of the CMU Sphinx 一 4 Decoder,”in Eighth Europe on Conference on Speech Communication and technology,2003 [Google Scholar]
  5. ong C, Oprea A, Biggio B, et al. Manipulating machine learning: Poisoning attacks and countermeasures for regression learning. In: Proc. of the 2018 IEEE Symp. on Security and Privacy (SP). 2018. 19-3 [Google Scholar]
  6. ong C, Nelson B, Sears R, et al. Can machine learning be secure? In: Proc. of the 2006 ACM Symp. on Information, Computer and Communications Security. 2006. 16-25 [Google Scholar]
  7. ong C, Karp B, Song D. Paragraph: Thwarting signature learning by training maliciously. In: Proc. of the Int’l Workshop on Recent Advances in Intrusion Detection. 2006. 81-105 [Google Scholar]

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