A Statistical Learning Based System for Fake Website Detection

TL;DR

This paper introduces a fake website detection system based on statistical learning theory that outperforms existing methods on a large test bed, demonstrating improved accuracy in identifying fake websites.

Contribution

The paper presents a novel fake website detection system utilizing SLT-based classification methods, showing superior performance over seven existing systems.

Findings

SLT-based system outperforms existing methods

Effective detection on a test bed of 900 websites

Demonstrates the potential of statistical learning in cybersecurity

Abstract

Existing fake website detection systems are unable to effectively detect fake websites. In this study, we advocate the development of fake website detection systems that employ classification methods grounded in statistical learning theory (SLT). Experimental results reveal that a prototype system developed using SLT-based methods outperforms seven existing fake website detection systems on a test bed encompassing 900 real and fake websites.