Zobrazeno 1 - 10
of 25
pro vyhledávání: '"Bimal Viswanath"'
Publikováno v:
IEEE Transactions on Software Engineering. :1-13
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04eabde7d77a6f1196b96587fd1f53b2
https://doi.org/10.1109/tse.2023.3265362
https://doi.org/10.1109/tse.2023.3265362
Publikováno v:
Proceedings of the ACM/IEEE 44th International Conference on Software Engineering: Companion Proceedings.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ef4bcf28178182d560054ec7cb090895
https://doi.org/10.1145/3510454.3528645
https://doi.org/10.1145/3510454.3528645
Publikováno v:
2022 IEEE/ACM 44th International Conference on Software Engineering: Companion Proceedings (ICSE-Companion).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::66774e2561e4b4a2034d07f633939c58
https://doi.org/10.1109/icse-companion55297.2022.9793808
https://doi.org/10.1109/icse-companion55297.2022.9793808
Autor:
Bimal Viswanath, Neal Mangaokar, Kavya Sundaram, Lauren Kelly, Bolun Wang, Mobin Javed, Parantapa Bhattacharya, Jiameng Pu
Publikováno v:
WWW
AI-manipulated videos, commonly known as deepfakes, are an emerging problem. Recently, researchers in academia and industry have contributed several (self-created) benchmark deepfake datasets, and deepfake detection algorithms. However, little effort
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::91a90d49ef4368b1a365be60c509ee21
http://arxiv.org/abs/2103.04263
http://arxiv.org/abs/2103.04263
Advances in deep neural networks (DNNs) have shown tremendous promise in the medical domain. However, the deep learning tools that are helping the domain, can also be used against it. Given the prevalence of fraud in the healthcare domain, it is impo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75a0ba38d3f899202ffc21bd335e9153
Publikováno v:
ACSAC
Recent advances in Generative Adversarial Networks (GANs) have significantly improved the quality of synthetic images or deepfakes. Photorealistic images generated by GANs start to challenge the boundary of human perception of reality, and brings new
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::25e47c5e2a75c7832ff48d26768cfea7
https://doi.org/10.1145/3427228.3427285
https://doi.org/10.1145/3427228.3427285
Autor:
Steve T.K. Jan, Bimal Viswanath, Tianrui Hu, Jiameng Pu, Gang Wang, Sonal Oswal, Qingying Hao
Publikováno v:
IEEE Symposium on Security and Privacy
Machine learning has been widely applied to building security applications. However, many machine learning models require the continuous supply of representative labeled data for training, which limits the models’ usefulness in practice. In this pa
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3d5659b2f61802464eaf74af74534e65
https://doi.org/10.1109/sp40000.2020.00079
https://doi.org/10.1109/sp40000.2020.00079
Publikováno v:
AsiaCCS
Phishing has been a big concern due to its active roles in recent data breaches and state-sponsored attacks. While existing works have extensively analyzed phishing websites and their operations, there is still a limited understanding of the informat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::991d647623bfd8ecacb59a075248ef4f
https://doi.org/10.1145/3321705.3329818
https://doi.org/10.1145/3321705.3329818
Publikováno v:
IEEE Symposium on Security and Privacy
Lack of transparency in deep neural networks (DNNs) make them susceptible to backdoor attacks, where hidden associations or triggers override normal classification to produce unexpected results. For example, a model with a backdoor always identifies
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::04988e0462351ad1c02e9313772afa54
https://doi.org/10.1109/sp.2019.00031
https://doi.org/10.1109/sp.2019.00031
Autor:
Ruichuan Chen, Christof Fetzer, Jörg Thalheim, Istemi Ekin Akkus, Lei Jiao, Pramod Bhatotia, Bimal Viswanath, Antonio Wendell De Oliveira Rodrigues
Publikováno v:
Middleware
Thalheim, J, Rodrigues, A, Akkuş, İ E, Bhatotia, P, Chen, R, Viswanath, B, Jiao, L & Fetzer, C 2017, Sieve: Actionable Insights from Monitored Metrics in Distributed Systems . in ACM/IFIP/USENIX Middleware 2017 . Las Vegas, Nevada, pp. 14-27, 18th ACM/IFIP/USENIX Middleware Conference, Las Vegas, Nevada, United States, 11/12/17 . https://doi.org/10.1145/3135974.3135977
Thalheim, J, Rodrigues, A, Akkuş, İ E, Bhatotia, P, Chen, R, Viswanath, B, Jiao, L & Fetzer, C 2017, Sieve: Actionable Insights from Monitored Metrics in Distributed Systems . in ACM/IFIP/USENIX Middleware 2017 . Las Vegas, Nevada, pp. 14-27, 18th ACM/IFIP/USENIX Middleware Conference, Las Vegas, Nevada, United States, 11/12/17 . https://doi.org/10.1145/3135974.3135977
Major cloud computing operators provide powerful monitoring tools to understand the current (and prior) state of the distributed systems deployed in their infrastructure. While such tools provide a detailed monitoring mechanism at scale, they also po
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1ae2520881b4c3a519bd8a691875bc6e
https://doi.org/10.1145/3135974.3135977
https://doi.org/10.1145/3135974.3135977