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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Dial "N" For Nxdomain: The Scale, Origin, And Security Implications Of Dns Queries To Non-Existent Domains, Gunnan Liu, Lin Jin, Shuai Hao, Yubao Zhang, Daiping Liu, Angelos Stavrou, Haining Wang
Dial "N" For Nxdomain: The Scale, Origin, And Security Implications Of Dns Queries To Non-Existent Domains, Gunnan Liu, Lin Jin, Shuai Hao, Yubao Zhang, Daiping Liu, Angelos Stavrou, Haining Wang
Computer Science Faculty Publications
Non-Existent Domain (NXDomain) is one type of the Domain Name System (DNS) error responses, indicating that the queried domain name does not exist and cannot be resolved. Unfortunately, little research has focused on understanding why and how NXDomain responses are generated, utilized, and exploited. In this paper, we conduct the first comprehensive and systematic study on NXDomain by investigating its scale, origin, and security implications. Utilizing a large-scale passive DNS database, we identify 146,363,745,785 NXDomains queried by DNS users between 2014 and 2022. Within these 146 billion NXDomains, 91 million of them hold historic WHOIS records, of which 5.3 million …
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Machine Learning-Based Event Generator For Electron-Proton Scattering, Y. Alanazi, P. Ambrozewicz, M. Battaglieri, A.N. Hiller Blin, M. P. Kuchera, Y. Li, T. Liu, R. E. Mcclellan, W. Melnitchouk, E. Pritchard, M. Robertson, N. Sato, R. Strauss, L. Velasco
Computer Science Faculty Publications
We present a new machine learning-based Monte Carlo event generator using generative adversarial networks (GANs) that can be trained with calibrated detector simulations to construct a vertex-level event generator free of theoretical assumptions about femtometer scale physics. Our framework includes a GAN-based detector folding as a fast-surrogate model that mimics detector simulators. The framework is tested and validated on simulated inclusive deep-inelastic scattering data along with existing parametrizations for detector simulation, with uncertainty quantification based on a statistical bootstrapping technique. Our results provide for the first time a realistic proof of concept to mitigate theory bias in inferring vertex-level event …