Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models

• URL: http://arxiv.org/abs/2507.07406v1
• Date: Thu, 10 Jul 2025 04:01:52 GMT
• Title: Phishing Detection in the Gen-AI Era: Quantized LLMs vs Classical Models
• Authors: Jikesh Thapa, Gurrehmat Chahal, Serban Voinea Gabreanu, Yazan Otoum,
• Abstract summary: Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency.<n>This paper presents a comparative evaluation of traditional Machine Learning (ML), Deep Learning (DL), and quantized small- parameter Large Language Models (LLMs) for phishing detection.<n>We show that while LLMs currently underperform compared to ML and DL methods in terms of raw accuracy, they exhibit strong potential for identifying subtle, context-based phishing cues.
• Score: 1.4999444543328293
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Phishing attacks are becoming increasingly sophisticated, underscoring the need for detection systems that strike a balance between high accuracy and computational efficiency. This paper presents a comparative evaluation of traditional Machine Learning (ML), Deep Learning (DL), and quantized small-parameter Large Language Models (LLMs) for phishing detection. Through experiments on a curated dataset, we show that while LLMs currently underperform compared to ML and DL methods in terms of raw accuracy, they exhibit strong potential for identifying subtle, context-based phishing cues. We also investigate the impact of zero-shot and few-shot prompting strategies, revealing that LLM-rephrased emails can significantly degrade the performance of both ML and LLM-based detectors. Our benchmarking highlights that models like DeepSeek R1 Distill Qwen 14B (Q8_0) achieve competitive accuracy, above 80%, using only 17GB of VRAM, supporting their viability for cost-efficient deployment. We further assess the models' adversarial robustness and cost-performance tradeoffs, and demonstrate how lightweight LLMs can provide concise, interpretable explanations to support real-time decision-making. These findings position optimized LLMs as promising components in phishing defence systems and offer a path forward for integrating explainable, efficient AI into modern cybersecurity frameworks.

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