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- W4297097376 abstract "The emergence of artificial intelligence [AI] , computer vision, and speech recognition systems have made significant growth in all areas of human life including cybersecurity. Multiple cybersecurity companies are trying to leverage AI to help combat cyberattacks as AI and machine learning can faster and cheaper monitor for any suspicious activity in the network, informing security specialists and network administrators only in case of a true emergency. As modern AI-powered systems are cooperating with human users by not only providing raw reports but also producing information based on text generation algorithms and text-to-speech functions, it is essential to ensure that this sensitive, security related information is not biased. In this pilot study, we are trying to showcase the problem of the perceived severity of a vulnerability by recipients speaking different native languages. Also, we are trying to answer the following question: how should an AI system present the information in order for the user to correctly understand the severity of the finding?" @default.
- W4297097376 created "2022-09-27" @default.
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- W4297097376 date "2022-09-07" @default.
- W4297097376 modified "2023-09-26" @default.
- W4297097376 title "Perceived severity of vulnerability in cybersecurity: cross linguistic variegation" @default.
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- W4297097376 doi "https://doi.org/10.1109/iccst52959.2022.9896488" @default.
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