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pro vyhledávání: '"Rafiq, Husnain"'
Autor:
Warner, Mark, Strohmayer, Angelika, Higgs, Matthew, Rafiq, Husnain, Yang, Liying, Coventry, Lynne
Growing evidence shows that proactive content moderation supported by AI can help improve online discourse. However, we know little about designing these systems, how design impacts efficacy and user experience, and how people perceive proactive mode
Externí odkaz:
http://arxiv.org/abs/2401.10627
Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at
Externí odkaz:
http://arxiv.org/abs/2210.02840
A myriad of recent literary works has leveraged generative adversarial networks (GANs) to generate unseen evasion samples. The purpose is to annex the generated data with the original train set for adversarial training to improve the detection perfor
Externí odkaz:
http://arxiv.org/abs/2109.08026
Publikováno v:
In Future Generation Computer Systems January 2024 150:294-302
Autor:
Rafiq, Husnain1 (AUTHOR) husnain.rafiq@northumbria.ac.uk, Aslam, Nauman1 (AUTHOR), Aleem, Muhammad2 (AUTHOR), Issac, Biju1 (AUTHOR), Randhawa, Rizwan Hamid1 (AUTHOR)
Publikováno v:
Scientific Reports. 11/14/2022, Vol. 12 Issue 1, p1-18. 18p.
Akademický článek
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Botnet detectors based on machine learning are potential targets for adversarial evasion attacks. Several research works employ adversarial training with samples generated from generative adversarial nets (GANs) to make the botnet detectors adept at
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0f33bc0274ce0dbc674d9f225c629adf
https://doi.org/10.2139/ssrn.4333338
https://doi.org/10.2139/ssrn.4333338
Akademický článek
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