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Machine learning model achieves 96% accuracy in phishing website detection

By PulseAugur Editorial · [1 sources] ·

A step-by-step guide details how to build a phishing website detector using PySpark and machine learning. The project utilized a dataset of 11,430 URLs, analyzing 89 features to identify malicious sites. The best-performing model, a Random Forest, achieved 96% accuracy and an AUC of 0.99, demonstrating machine learning's effectiveness in distinguishing fake websites from legitimate ones based on URL characteristics and page signals. AI

IMPACT Demonstrates how machine learning can be applied to enhance cybersecurity by detecting phishing websites with high accuracy.

RANK_REASON The item describes a technical guide and a machine learning model for a specific application (phishing detection), which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]

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Machine learning model achieves 96% accuracy in phishing website detection

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41 / 100
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The item describes a technical guide and a machine learning model for a specific application (phishing detection), which falls under research. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. Towards AI TIER_1 English(EN) · Mala Deep ·

    Phishing Website Detection with Machine Learning: A Step-by-Step PySpark Guide

    <h4><em>A step-by-step guide to building a fake-domain detector with PySpark, MLlib and Tableau, using 11,430 real URLs. My best model hit 96% accuracy.</em></h4><figure><img alt="Phishing website detection with machine learning: a fake bank login page with a suspicious URL flagg…

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