Zobrazeno 1 - 10
of 658
pro vyhledávání: '"P. Tihanyi"'
Integrating Deep Learning (DL) techniques in the Internet of Vehicles (IoV) introduces many security challenges and issues that require thorough examination. This literature review delves into the inherent vulnerabilities and risks associated with DL
Externí odkaz:
http://arxiv.org/abs/2407.16410
Autor:
Ferrag, Mohamed Amine, Alwahedi, Fatima, Battah, Ammar, Cherif, Bilel, Mechri, Abdechakour, Tihanyi, Norbert
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs). We explore LLM applications across various domains, including hardware design security, intrusion detection, software en
Externí odkaz:
http://arxiv.org/abs/2405.12750
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
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:
Menezes, Rafael, Aldughaim, Mohannad, Farias, Bruno, Li, Xianzhiyu, Manino, Edoardo, Shmarov, Fedor, Song, Kunjian, Brauße, Franz, Gadelha, Mikhail R., Tihanyi, Norbert, Korovin, Konstantin, Cordeiro, Lucas C.
ESBMC implements many state-of-the-art techniques for model checking. We report on new and improved features that allow us to obtain verification results for previously unsupported programs and properties. ESBMC employs a new static interval analysis
Externí odkaz:
http://arxiv.org/abs/2312.14746
Autor:
Ferrag, Mohamed Amine, Battah, Ammar, Tihanyi, Norbert, Jain, Ridhi, Maimut, Diana, Alwahedi, Fatima, Lestable, Thierry, Thandi, Narinderjit Singh, Mechri, Abdechakour, Debbah, Merouane, Cordeiro, Lucas C.
Software vulnerabilities can cause numerous problems, including crashes, data loss, and security breaches. These issues greatly compromise quality and can negatively impact the market adoption of software applications and systems. Traditional bug-fix
Externí odkaz:
http://arxiv.org/abs/2307.06616
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
Autor:
Ferrag, Mohamed Amine, Ndhlovu, Mthandazo, Tihanyi, Norbert, Cordeiro, Lucas C., Debbah, Merouane, Lestable, Thierry, Thandi, Narinderjit Singh
The field of Natural Language Processing (NLP) is currently undergoing a revolutionary transformation driven by the power of pre-trained Large Language Models (LLMs) based on groundbreaking Transformer architectures. As the frequency and diversity of
Externí odkaz:
http://arxiv.org/abs/2306.14263
Autor:
Ferrag, Mohamed Amine, Friha, Othmane, Kantarci, Burak, Tihanyi, Norbert, Cordeiro, Lucas, Debbah, Merouane, Hamouda, Djallel, Al-Hawawreh, Muna, Choo, Kim-Kwang Raymond
The ongoing deployment of the fifth generation (5G) wireless networks constantly reveals limitations concerning its original concept as a key driver of Internet of Everything (IoE) applications. These 5G challenges are behind worldwide efforts to ena
Externí odkaz:
http://arxiv.org/abs/2306.10309
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