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MNN-XSS: Modular Neural Network Based Approach for XSS Attack Detection

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journal contribution
posted on 19.11.2021, 04:16 by Ahmed Abdullah Alqarni, Nizar Alsharif, Nayeem Ahmad Khan, Lilia Georgieva, Eric PardedeEric Pardede, Mohammed Y. Alzahrani
Abstract: The rapid growth and uptake of network-based communication technologies have made cybersecurity a significant challenge as the number of cyber-attacks is also increasing. A number of detection systems are used in an attempt to detect known attacks using signatures in network traffic. In recent years, researchers have used different machine learning methods to detect network attacks without relying on those signatures. The methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection product. In this study, we propose and implement a new method that relies on a modular deep neural network for reducing the false positive rate in the XSS attack detection system. Experiments were performed using a dataset consists of 1000 malicious and 10000 benign sample. The model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS attacks. The results obtained from the experiments depict improvement in the detection accuracy as high as 99.96% compared to other approaches.

History

Publication Date

01/01/2022

Journal

Computers, Materials and Continua

Volume

70

Issue

2

Pagination

11p. (p. 4075-4085)

Publisher

Tech Science Press

ISSN

1546-2218

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The Author reserves all moral rights over the deposited text and must be credited if any re-use occurs. Documents deposited in OPAL are the Open Access versions of outputs published elsewhere. Changes resulting from the publishing process may therefore not be reflected in this document. The final published version may be obtained via the publisher’s DOI. Please note that additional copyright and access restrictions may apply to the published version.

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