activity
20242026
most citedCan We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs

7 citations · 13 across the 9 of their papers we have counts for

collaborators

10 papers

cs.CR2026

NeuroGraph: An AI Graph-Driven Neuro-Symbolic Framework for Explainable Threat Reasoning in Advanced Manufacturing

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim +2

The growing complexity of cyber-physical attack surfaces in advanced manufacturing has made cyber threat intelligence analysis increasingly difficult. Although large language model…

cs.AI2026

Enhancing LLMs with Context-Specific Knowledge for Mitigating Misinformation in SMEs: A RAG-based Modeling and Analysis

Md. Samiul Islam, Iqbal H. Sarker, Chadni Islam +3

Large Language Models (LLMs), a part of artificial intelligence (AI), are increasingly being adopted by Small and Medium Enterprises (SMEs) to enhance question-answering capabiliti…

cs.CR2026

DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense

Zhen Wang, Kristen Moore, Qin Wang +7

Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to h…

cs.CR2026

Enhancing Adversarial Robustness in Network Intrusion Detection: A Layer-wise Adaptive Regularization Approach

Hira Nasir, Eiman Javed, Balawal Shabir +2

The new wave of adversarial attacks that utilize gradient-related vulnerabilities in neural network-based classifiers makes Network Intrusion Detection Systems more open to such th…

cs.CR2026

TempoNet: Learning Realistic Communication and Timing Patterns for Network Traffic Simulation

Kristen Moore, Diksha Goel, Cody James Christopher +5

Realistic network traffic simulation is critical for evaluating intrusion detection systems, stress-testing network protocols, and constructing high-fidelity environments for cyber…

cs.CR2025

BRIDG-ICS: AI-Grounded Knowledge Graphs for Intelligent Threat Analytics in Industry~5.0 Cyber-Physical Systems

Padmeswari Nandiya, Ahmad Mohsin, Ahmed Ibrahim +2

Industry 5.0's increasing integration of IT and OT systems is transforming industrial operations but also expanding the cyber-physical attack surface. Industrial Control Systems (I…

cs.LG2025★ 1 cited

SME-TEAM: Leveraging Trust and Ethics for Secure and Responsible Use of AI and LLMs in SMEs

Iqbal H. Sarker, Helge Janicke, Ahmad Mohsin +1

Artificial Intelligence (AI) and Large Language Models (LLMs) are revolutionizing today's business practices; however, their adoption within small and medium-sized enterprises (SME…

cs.AI2025★ 5 cited

A Unified Framework for Human AI Collaboration in Security Operations Centers with Trusted Autonomy

Ahmad Mohsin, Helge Janicke, Ahmed Ibrahim +2

This article presents a structured framework for Human-AI collaboration in Security Operations Centers (SOCs), integrating AI autonomy, trust calibration, and Human-in-the-loop dec…

cs.CR2025

CyberAlly: Leveraging LLMs and Knowledge Graphs to Empower Cyber Defenders

Minjune Kim, Jeff Wang, Kristen Moore +7

The increasing frequency and sophistication of cyberattacks demand innovative approaches to strengthen defense capabilities. Training on live infrastructure poses significant risks…

cs.CR2024★ 7 cited

Can We Trust Large Language Models Generated Code? A Framework for In-Context Learning, Security Patterns, and Code Evaluations Across Diverse LLMs

Ahmad Mohsin, Helge Janicke, Adrian Wood +3

Large Language Models (LLMs) such as ChatGPT and GitHub Copilot have revolutionized automated code generation in software engineering. However, as these models are increasingly uti…