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pro vyhledávání: '"Berend, David"'
Autor:
Berend, David
As Deep Learning (DL) is continuously adopted in many safety critical applications, its quality and reliability start to raise concerns. Similar to the traditional software development process, testing the DL software to uncover its defects at an ear
Externí odkaz:
http://arxiv.org/abs/2105.02540
Network intrusion attacks are a known threat. To detect such attacks, network intrusion detection systems (NIDSs) have been developed and deployed. These systems apply machine learning models to high-dimensional vectors of features extracted from net
Externí odkaz:
http://arxiv.org/abs/2103.06297
The performance of a machine learning-based malware classifier depends on the large and updated training set used to induce its model. In order to maintain an up-to-date training set, there is a need to continuously collect benign and malicious files
Externí odkaz:
http://arxiv.org/abs/2010.16323
Autor:
Cao, Yushi, Berend, David, Tolmach, Palina, Amit, Guy, Levy, Moshe, Liu, Yang, Shabtai, Asaf, Elovici, Yuval
Deep learning-based facial recognition systems have experienced increased media attention due to exhibiting unfair behavior. Large enterprises, such as IBM, shut down their facial recognition and age prediction systems as a consequence. Age predictio
Externí odkaz:
http://arxiv.org/abs/2009.05283
Akademický článek
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Publikováno v:
ACM International Conference Proceeding Series; 8/27/2018, p1-10, 10p
