all InfoSec news
Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning. (arXiv:2205.01992v2 [cs.LG] UPDATED)
Sept. 2, 2022, 1:20 a.m. | Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis, Sebastiano Vascon, Werner Zellinger, Bernhard A. Moser, Alina Oprea, Battista Biggio, Marc
cs.CR updates on arXiv.org arxiv.org
The success of machine learning is fueled by the increasing availability of
computing power and large training datasets. The training data is used to learn
new models or update existing ones, assuming that it is sufficiently
representative of the data that will be encountered at test time. This
assumption is challenged by the threat of poisoning, an attack that manipulates
the training data to compromise the model's performance at test time. Although
poisoning has been acknowledged as a relevant threat …
data data poisoning machine machine learning patterns poisoning reloaded security survey training
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Security Engineers
@ D. E. Shaw Research | New York City
Technology Security Analyst
@ Halton Region | Oakville, Ontario, Canada
Senior Cyber Security Analyst
@ Valley Water | San Jose, CA
Senior Application Security Engineer, Application Security
@ Miro | Amsterdam, NL
SOC Analyst (m/w/d)
@ LANXESS | Leverkusen, NW, DE, 51373
Lead Security Solutions Engineer (Remote, North America)
@ Dynatrace | Waltham, MA, United States