activity
20172026
most citedReasoning Beyond Limits: Advances and Open Problems for LLMs

22 citations · 43 across the 29 of their papers we have counts for

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Showing 2024Show all

8 papers · 1 filter

cs.AI2024

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…

cs.CR2024

GenDFIR: Advancing Cyber Incident Timeline Analysis Through Retrieval Augmented Generation and Large Language Models

Fatma Yasmine Loumachi, Mohamed Chahine Ghanem, Mohamed Amine Ferrag

Cyber timeline analysis, or forensic timeline analysis, is crucial in Digital Forensics and Incident Response (DFIR). It examines artefacts and events particularly timestamps and m…

cs.CR2024

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…

cs.CR20248 cited

Critical Infrastructure Protection: Generative AI, Challenges, and Opportunities

Yagmur Yigit, Mohamed Amine Ferrag, Iqbal H. Sarker +4

Critical National Infrastructure (CNI) encompasses a nation's essential assets that are fundamental to the operation of society and the economy, ensuring the provision of vital uti…

cs.CR2024

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…

cs.CR2024

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…