57 citations · 163 across the 20 of their papers we have counts for
5 papers · 1 filter
Dynamic Intelligence Assessment: Benchmarking LLMs on the Road to AGI with a Focus on Model Confidence
Norbert Tihanyi, Tamas Bisztray, Richard A. Dubniczky +11
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…
Securing Tomorrow's Smart Cities: Investigating Software Security in Internet of Vehicles and Deep Learning Technologies
Ridhi Jain, Norbert Tihanyi, Mohamed Amine Ferrag
Integrating Deep Learning (DL) techniques in the Internet of Vehicles (IoV) introduces many security challenges and issues that require thorough examination. This literature review…
Generative AI in Cybersecurity: A Comprehensive Review of LLM Applications and Vulnerabilities
Mohamed Amine Ferrag, Fatima Alwahedi, Ammar Battah +5
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…
How secure is AI-generated Code: A Large-Scale Comparison of Large Language Models
Norbert Tihanyi, Tamas Bisztray, Mohamed Amine Ferrag +2
This study compares state-of-the-art Large Language Models (LLMs) on their tendency to generate vulnerabilities when writing C programs using a neutral zero-shot prompt. Tihanyi et…
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
Norbert Tihanyi, Mohamed Amine Ferrag, Ridhi Jain +2
Large Language Models (LLMs) are increasingly used across various domains, from software development to cyber threat intelligence. Understanding all the different fields of cyberse…