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pro vyhledávání: '"Bisztray, Tamás"'
Autor:
Tihanyi, Norbert, Bisztray, Tamas, Dubniczky, Richard A., Toth, Rebeka, Borsos, Bertalan, Cherif, Bilel, Ferrag, Mohamed Amine, Muzsai, Lajos, Jain, Ridhi, Marinelli, Ryan, Cordeiro, Lucas C., Debbah, Merouane, Mavroeidis, Vasileios, Josang, Audun
As machine intelligence evolves, the need to test and compare the problem-solving abilities of different AI models grows. However, current benchmarks are often simplistic, allowing models to perform uniformly well and making it difficult to distingui
Externí odkaz:
http://arxiv.org/abs/2410.15490
This study provides a comparative analysis of state-of-the-art large language models (LLMs), analyzing how likely they generate vulnerabilities when writing simple C programs using a neutral zero-shot prompt. We address a significant gap in the liter
Externí odkaz:
http://arxiv.org/abs/2404.18353
LLMs in Web Development: Evaluating LLM-Generated PHP Code Unveiling Vulnerabilities and Limitations
This study evaluates the security of web application code generated by Large Language Models, analyzing 2,500 GPT-4 generated PHP websites. These were deployed in Docker containers and tested for vulnerabilities using a hybrid approach of Burp Suite
Externí odkaz:
http://arxiv.org/abs/2404.14459
Large Language Models (LLMs) are increasingly used across various domains, from software development to cyber threat intelligence. Understanding all the different fields of cybersecurity, which includes topics such as cryptography, reverse engineerin
Externí odkaz:
http://arxiv.org/abs/2402.07688
Autor:
Tihanyi, Norbert, Bisztray, Tamas, Jain, Ridhi, Ferrag, Mohamed Amine, Cordeiro, Lucas C., Mavroeidis, Vasileios
Publikováno v:
PROMISE 2023: Proceedings of the 19th International Conference on Predictive Models and Data Analytics in Software Engineering December 2023 Pages 33 to 43
This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification. We introduce a dynamic zero-shot prompting technique constructed to spawn diverse programs ut
Externí odkaz:
http://arxiv.org/abs/2307.02192
Using the computational resources of an untrusted third party to crack a password hash can pose a high number of privacy and security risks. The act of revealing the hash digest could in itself negatively impact both the data subject who created the
Externí odkaz:
http://arxiv.org/abs/2306.08740
Publikováno v:
2021 International Conference of the Biometrics Special Interest Group (BIOSIG), 2021, pp. 1-5
Technological advancements allow biometric applications to be more omnipresent than in any other time before. This paper argues that in the current EU data protection regulation, classification applications using biometric data receive less protectio
Externí odkaz:
http://arxiv.org/abs/2211.12899
Autor:
Bisztray, Tamas, Gruschka, Nils
Publikováno v:
NordSec 2019. Lecture Notes in Computer Science, vol 11875. Springer, Cham
Privacy and data protection have become more and more important in recent years since an increasing number of enterprises and startups are harvesting personal data as a part of their business model. One central requirement of the GDPR is the implemen
Externí odkaz:
http://arxiv.org/abs/2110.07366
Autor:
Ezer, Péter, Szűcs, Kitti, Lukács, Réka, Bisztray, Tamás, Vilmányi, Gábor, Szokodi, István, Komócsi, András, Kónyi, Attila
Publikováno v:
Journal of Clinical Medicine; Oct2024, Vol. 13 Issue 19, p5742, 17p
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