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URL Based File Inclusion Attack Behavior Analysis and An Autoencoder Detection Model

Authors:

Kun MengAuthors Info & Claims

ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence

Pages 252 - 258

https://doi.org/10.1145/3532213.3532251

Published: 13 July 2022 Publication History

Abstract

The development of network and communication technologies and the construction of information technology in universities have improved the efficiency of learning and work of teachers and students, while at the same time, unscrupulous elements have used a variety of attack methods to pose a great threat to the interests of schools and students. hackers can exploit vulnerabilities in web applications to illegally gain access, inject malicious code, steal information and more. File inclusion attack is one of the main types of attacks on the Web. For the file inclusion attack, this paper firstly analyses its attack method and its performance on the url, and secondly extracts the relevant features based on the real campus network traffic data to form a feature dataset. In view of the unbalanced data and sparse abnormal samples and the characteristics of the real feature data, a detection method based on the autoencoder model and the percentage threshold selection method is designed. The experimental evaluation results show that the classification accuracy of the method can reach as high as 95.3%, this method can effectively detect the traffic containing file inclusion attack and provide a new idea for the detection of file inclusion attack.

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Cited By

Goyal AMatta P(2023)Beyond the Basics: A Study of Advanced Techniques for Detecting and Preventing SQL Injection Attacks2023 4th International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC58147.2023.10276077(628-631)Online publication date: 20-Sep-2023
https://doi.org/10.1109/ICOSEC58147.2023.10276077

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ICCAI '22: Proceedings of the 8th International Conference on Computing and Artificial Intelligence

March 2022

809 pages

ISBN:9781450396110

DOI:10.1145/3532213

Copyright © 2022 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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New York, NY, United States

Publication History

Published: 13 July 2022

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Normal Project of Science and Technology at Beijing Information Science and Technology University

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ICCAI '22

ICCAI '22: 2022 8th International Conference on Computing and Artificial Intelligence

March 18 - 21, 2022

Tianjin, China

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Cited By

Goyal AMatta P(2023)Beyond the Basics: A Study of Advanced Techniques for Detecting and Preventing SQL Injection Attacks2023 4th International Conference on Smart Electronics and Communication (ICOSEC)10.1109/ICOSEC58147.2023.10276077(628-631)Online publication date: 20-Sep-2023
https://doi.org/10.1109/ICOSEC58147.2023.10276077

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