受强制性开放获取政策约束的文章 - Sean Oesch了解详情
可在其他位置公开访问的文章：15 篇
Systematization of password manageruse cases and design paradigms
J Simmons, O Diallo, S Oesch, S Ruoti
Proceedings of the 37th Annual Computer Security Applications Conference …, 2021
强制性开放获取政策： US Department of Energy
Testing SOAR tools in use
RA Bridges, AE Rice, S Oesch, JA Nichols, C Watson, K Spakes, S Norem, ...
Computers & Security 129, 103201, 2023
强制性开放获取政策： US Department of Energy, US Department of Defense
An assessment of the usability of machine learning based tools for the security operations center
S Oesch, R Bridges, J Smith, J Beaver, J Goodall, K Huffer, C Miles, ...
2020 International Conferences on Internet of Things (iThings) and IEEE …, 2020
强制性开放获取政策： US Department of Energy, US Department of Defense
Understanding user perceptions of security and privacy for group chat: a survey of users in the US and UK
S Oesch, R Abu-Salma, O Diallo, J Krämer, J Simmons, J Wu, S Ruoti
Proceedings of the 36th Annual Computer Security Applications Conference …, 2020
强制性开放获取政策： German Research Foundation
Toward the detection of polyglot files
L Koch, S Oesch, A Chaulagain, M Adkisson, S Erwin, B Weber
Proceedings of the 15th Workshop on Cyber Security Experimentation and Test …, 2022
强制性开放获取政策： US Department of Energy
User Perceptions of Security and Privacy for Group Chat
S Oesch, R Abu-Salma, O Diallo, J Krämer, J Simmons, J Wu, S Ruoti
Digital Threats: Research and Practice (DTRAP) 3 (2), 1-29, 2022
强制性开放获取政策： German Research Foundation
D2U: Data driven user emulation for the enhancement of cyber testing, training, and data set generation
S Oesch, RA Bridges, M Verma, B Weber, O Diallo
Proceedings of the 14th Cyber Security Experimentation and Test Workshop, 17-26, 2021
强制性开放获取政策： US Department of Energy, US Department of Defense
A mathematical framework for evaluation of SOAR tools with limited survey data
S Norem, AE Rice, S Erwin, RA Bridges, S Oesch, B Weber
European Symposium on Research in Computer Security, 557-575, 2021
强制性开放获取政策： US Department of Energy, US Department of Defense
Beyond the Hype: An Evaluation of Commercially Available Machine Learning–based Malware Detectors
RA Bridges, S Oesch, MD Iannacone, KMT Huffer, B Jewell, JA Nichols, ...
Digital Threats: Research and Practice 4 (2), 1-22, 2023
强制性开放获取政策： US Department of Energy, US Department of Defense
An Integrated Platform for Collaborative Data Analytics
S Oesch, R Gillen, T Karnowski
2020 International Conferences on Internet of Things (iThings) and IEEE …, 2020
强制性开放获取政策： US Department of Energy
Reinforcement Learning-based Traffic Control to Optimize Energy Usage and Throughput (CRADA report)
TP Karnowski, M Eicholtz, W Elwasif, R Ferrell, T Naughton III, TS Oesch, ...
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2019
强制性开放获取政策： US Department of Energy
AI ATAC 1: An Evaluation of Prominent Commercial Malware Detectors
RA Bridges, B Weber, JM Beaver, JM Smith, ME Verma, S Norem, ...
2023 IEEE International Conference on Big Data (BigData), 1620-1629, 2023
强制性开放获取政策： US Department of Energy, US Department of Defense
Estimating vehicle fuel economy from overhead camera imagery and application for traffic control
T Karnowski, R Tokola, S Oesch, M Eicholtz, J Price, T Gee
Electronic Imaging 32, 1-7, 2020
强制性开放获取政策： US Department of Energy
Integrating Overhead Camera Imagery with Reinforcement Learning to Improve Fuel Economy Through Adaptive Traffic Control
T Karnowski, R Tokola, T Oesch, J Price, T Gee
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2020
强制性开放获取政策： US Department of Energy
ShareAnalytics
TS Oesch, RE Gillen, NQ Haas, TP Karnowski
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2018
强制性开放获取政策： US Department of Energy
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