Performance Analysis of Different ML/DL algorithms for Detecting Web Attacks
Abstract
In the digital era, where web applications serve as
the backbone for numerous services, the security of
online systems has become a critical concern. Web
attacks such as SQL injection, cross-site scripting
(XSS), denial-of-service (DoS), and phishing have
become increasingly sophisticated, threatening the
integrity, confidentiality, and availability of webbased
platforms. Detecting these threats promptly
is crucial to minimizing damage and securing data.
Traditional security mechanisms like firewalls and
rule-based systems often struggle to detect novel or
obfuscated attacks, prompting the need for more
adaptive and intelligent detection techniques.
Machine Learning (ML) and Deep Learning (DL)
models have emerged as promising tools for
identifying patterns and anomalies within network
traffic and web application logs.
This project aims to perform a comparative
analysis of various ML and DL algorithms, such as
Support Vector Machine (SVM), Decision Trees,
Random Forests, Convolutional Neural Networks
(CNN), and Recurrent Neural Networks (RNN), to
evaluate their performance in detecting different
types of web attacks.
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References
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