activity
20242026
most citedDetecting Quishing Attacks with Machine Learning Techniques Through QR Code Analysis

5 citations · 5 across the 2 of their papers we have counts for

collaborators

8 papers

cs.AR2026

Cloud to Edge: Benchmarking LLM Inference On Hardware-Accelerated Single-Board Computers

Harri Renney, Fouad Trad, Michael Mattarock +2

Large language models (LLMs) are becoming increasingly capable at small parameter scales. At the same time, conventional cloud-centric deployment introduces challenges around data…

cs.CR20265 cited

Detecting Quishing Attacks with Machine Learning Techniques Through QR Code Analysis

Fouad Trad, Ali Chehab

The rise of QR code-based phishing ("Quishing") poses a growing cybersecurity threat, as attackers increasingly exploit QR codes to bypass traditional phishing defenses. Existing d…

cs.IR2025

Chained Prompting for Better Systematic Review Search Strategies

Fatima Nasser, Fouad Trad, Ammar Mohanna +2

Systematic reviews require the use of rigorously designed search strategies to ensure both comprehensive retrieval and minimization of bias. Conventional manual approaches, althoug…

cs.SE2025

Retrieval-Augmented Few-Shot Prompting Versus Fine-Tuning for Code Vulnerability Detection

Fouad Trad, Ali Chehab

Few-shot prompting has emerged as a practical alternative to fine-tuning for leveraging the capabilities of large language models (LLMs) in specialized tasks. However, its effectiv…

cs.CR2025

CLASP: Cost-Optimized LLM-based Agentic System for Phishing Detection

Fouad Trad, Ali Chehab

Phishing websites remain a significant cybersecurity threat, necessitating accurate and cost-effective detection mechanisms. In this paper, we present CLASP, a novel system that ef…

cs.CL2024

Streamlining Systematic Reviews: A Novel Application of Large Language Models

Fouad Trad, Ryan Yammine, Jana Charafeddine +4

Systematic reviews (SRs) are essential for evidence-based guidelines but are often limited by the time-consuming nature of literature screening. We propose and evaluate an in-house…