# Phishing Webpage Detection: Unveiling the Threat Landscape and Investigating Detection Techniques

arXiv: https://arxiv.org/abs/2509.08424 · HTML: https://mlsanity.com/paper/2509.08424/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2025.

## Built on by (most cited first, 1 of 1)

- [MemoPhishAgent: Memory-Augmented Multi-Modal LLM Agent for Phishing URL Detection](https://mlsanity.com/paper/2602.21394/) (2026)

## Abstract (arXiv)

In the realm of cybersecurity, phishing stands as a prevalent cyber attack, where attackers employ various tactics to deceive users into gathering their sensitive information, potentially leading to identity theft or financial gain. Researchers have been actively working on advancing phishing webpage detection approaches to detect new phishing URLs, bolstering user protection. Nonetheless, the ever-evolving strategies employed by attackers, aimed at circumventing existing detection approaches and tools, present an ongoing challenge to the research community. This survey presents a systematic categorization of diverse phishing webpage detection approaches, encompassing URL-based, webpage content-based, and visual techniques. Through a comprehensive review of these approaches and an in-depth analysis of existing literature, our study underscores current research gaps in phishing webpage detection. Furthermore, we suggest potential solutions to address some of these gaps, contributing valuable insights to the ongoing efforts to combat phishing attacks.