WEB ATTACK DETECTION COMPARATIVE ANALYSIS OF LSTM AND DNN-BASED DEFENSE MODELS WITH CORRESPONDENCE ANALYSIS

Jaehyung Park; Junghee Park

doi:10.20319/stra.2025.1024

Authors

Jaehyung Park Division of Smart Cities, Korea University, Sejong, South Korea
Junghee Park Cognitive Engineering Lab, Yonsei University, Seoul, South Korea

DOI:

https://doi.org/10.20319/stra.2025.1024

Abstract

Recently since there exists more companies using web, web attacks to hijacking or manipulate the privacy information have increased. Among web vulnerability OWASP has introduced, SQL injection, XSS, File Inclusion have constantly occurred through more than a decade. It concludes that web servers have trouble with blocking old-fashioned web vulnerabilities. This paper is going to skim through web attack defending methods and compares existing web attack detection machine learning models and new ensemble model DPL with ANOVA, chi-square analysis, correspondence analysis to find out relativity between model and web attack. As result of correspondence analysis, brand new model DPL excels existing models but even DPL model have low relativity on XSS. It is expected that post research must introduce more XSS relevant model.

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