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New MLP framework detects phishing URLs with 99.24% accuracy

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a new, efficient framework for detecting phishing URLs in real-time. This system uses a hybrid approach, combining blacklist screening with a Multi-Layer Perceptron (MLP) classifier that analyzes structural URL features. It achieves high accuracy and speed by avoiding complex content analysis or external API calls, making it suitable for resource-limited environments. A prototype application called CyberGuard demonstrates its practical deployment potential. AI

IMPACT This research offers a more efficient and accurate method for real-time phishing URL detection, potentially improving cybersecurity defenses.

RANK_REASON This is a research paper detailing a new framework for phishing URL detection. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New MLP framework detects phishing URLs with 99.24% accuracy

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This is a research paper detailing a new framework for phishing URL detection. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Uche Unoke Emmanuel, Gideon Francis Oghie ·

    A Lightweight Hybrid MLP-Based Framework for Real-Time Phishing URL Detection Using Structural URL Features

    arXiv:2606.00889v1 Announce Type: cross Abstract: Phishing attacks remain a major cybersecurity threat, exploiting deceptive URLs to steal sensitive user information. Traditional blacklist and rule-based detection approaches are reactive and often fail to identify newly emerging …

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