9 papers
Does Teaming-Up LLMs Improve Secure Code Generation? A Comprehensive Evaluation with Multi-LLMSecCodeEval
Bushra Sabir, Shigang Liu, Seung Ick Jang +6
Automatically generating source code from natural language using large language models (LLMs) is becoming common, yet security vulnerabilities persist despite advances in fine tuni…
Improving Methodologies for Agentic Evaluations Across Domains: Leakage of Sensitive Information, Fraud and Cybersecurity Threats
Ee Wei Seah, Yongsen Zheng, Naga Nikshith +67
The rapid rise of autonomous AI systems and advancements in agent capabilities are introducing new risks due to reduced oversight of real-world interactions. Yet agent testing rema…
From Description to Detection: LLM based Extendable O-RAN Compliant Blind DoS Detection in 5G and Beyond
Thusitha Dayaratne, Ngoc Duy Pham, Viet Vo +5
The quality and experience of mobile communication have significantly improved with the introduction of 5G, and these improvements are expected to continue beyond the 5G era. Howev…
Setup Once, Secure Always: A Single-Setup Secure Federated Learning Aggregation Protocol with Forward and Backward Secrecy for Dynamic Users
Nazatul Haque Sultan, Yan Bo, Yansong Gao +10
Federated Learning (FL) enables multiple users to collaboratively train a machine learning model without sharing raw data, making it suitable for privacy-sensitive applications. Ho…
Robust Anomaly Detection in O-RAN: Leveraging LLMs against Data Manipulation Attacks
Thusitha Dayaratne, Ngoc Duy Pham, Viet Vo +5
The introduction of 5G and the Open Radio Access Network (O-RAN) architecture has enabled more flexible and intelligent network deployments. However, the increased complexity and o…
Active Attack Resilience in 5G: A New Take on Authentication and Key Agreement
Nazatul H. Sultan, Xinlong Guan, Josef Pieprzyk +3
As 5G networks expand into critical infrastructure, secure and efficient user authentication is more important than ever. The 5G-AKA protocol, standardized by 3GPP in TS 33.501, is…