Open Access

ARTICLE

A Web Application Fingerprint Recognition Method Based on Machine Learning

Yanmei Shi¹, Wei Yu^2,*, Yanxia Zhao^3,*, Yungang Jia⁴

1 School of Integrated Circuit Science and Engineering, Tianjin University of Technology, Tianjin, 300382, China
2 College of International Business, Zhejiang Yuexiu University, Shaoxing, 312030, China
3 School of Public Administration, Zhejiang Gongshang University, Hangzhou, 310018, China
4 Tianjin Branch of National Computer Network Emergency Response Technical Team/Coordination Center of China, Tianjin, 300100, China

* Corresponding Authors: Wei Yu. Email: ; Yanxia Zhao. Email:

(This article belongs to the Special Issue: Machine Learning Empowered Distributed Computing: Advance in Architecture, Theory and Practice)

Computer Modeling in Engineering & Sciences 2024, 140(1), 887-906. https://doi.org/10.32604/cmes.2024.046140

Received 20 September 2023; Accepted 06 February 2024; Issue published 16 April 2024

Abstract

Web application fingerprint recognition is an effective security technology designed to identify and classify web applications, thereby enhancing the detection of potential threats and attacks. Traditional fingerprint recognition methods, which rely on preannotated feature matching, face inherent limitations due to the ever-evolving nature and diverse landscape of web applications. In response to these challenges, this work proposes an innovative web application fingerprint recognition method founded on clustering techniques. The method involves extensive data collection from the Tranco List, employing adjusted feature selection built upon Wappalyzer and noise reduction through truncated SVD dimensionality reduction. The core of the methodology lies in the application of the unsupervised OPTICS clustering algorithm, eliminating the need for preannotated labels. By transforming web applications into feature vectors and leveraging clustering algorithms, our approach accurately categorizes diverse web applications, providing comprehensive and precise fingerprint recognition. The experimental results, which are obtained on a dataset featuring various web application types, affirm the efficacy of the method, demonstrating its ability to achieve high accuracy and broad coverage. This novel approach not only distinguishes between different web application types effectively but also demonstrates superiority in terms of classification accuracy and coverage, offering a robust solution to the challenges of web application fingerprint recognition.

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Keywords

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