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Found 9 papers, 3 papers with code
Generative AI and Large Language Models for Cyber Security: All Insights You Need
This paper provides a comprehensive review of the future of cybersecurity through Generative AI and Large Language Models (LLMs).
Do Neutral Prompts Produce Insecure Code? FormAI-v2 Dataset: Labelling Vulnerabilities in Code Generated by Large Language Models
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.
CyberMetric: A Benchmark Dataset based on Retrieval-Augmented Generation for Evaluating LLMs in Cybersecurity Knowledge
The results can serve as a reference for comparing the general cybersecurity knowledge of humans and LLMs.
SecureFalcon: Are We There Yet in Automated Software Vulnerability Detection with LLMs?
SecureFalcon achieves 94% accuracy in binary classification and up to 92% in multiclassification, with instant CPU inference times.
The FormAI Dataset: Generative AI in Software Security Through the Lens of Formal Verification
This paper presents the FormAI dataset, a large collection of 112, 000 AI-generated compilable and independent C programs with vulnerability classification.
Revolutionizing Cyber Threat Detection with Large Language Models: A privacy-preserving BERT-based Lightweight Model for IoT/IIoT Devices
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.
A New Era in Software Security: Towards Self-Healing Software via Large Language Models and Formal Verification
Initially, we employ Bounded Model Checking (BMC) to identify vulnerabilities and extract counterexamples.
Poisoning Attacks in Federated Edge Learning for Digital Twin 6G-enabled IoTs: An Anticipatory Study
However, we need to also consider the potential of attacks targeting the underlying AI systems (e. g., adversaries seek to corrupt data on the IoT devices during local updates or corrupt the model updates); hence, in this article, we propose an anticipatory study for poisoning attacks in federated edge learning for digital twin 6G-enabled IoT environments.
A novel Two-Factor HoneyToken Authentication Mechanism
The majority of systems rely on user authentication on passwords, but passwords have so many weaknesses and widespread use that easily raise significant security concerns, regardless of their encrypted form.
Cryptography and Security
