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

Ahmed Abdullah Alqarni1, Nizar Alsharif1, Nayeem Ahmad Khan1,*, Lilia Georgieva2, Eric Pardade3, Mohammed Y. Alzahrani1

1 Department of Computer Sciences and Information Technology, AlBaha University, AlBaha, Saudi Arabia
2 Department of Computer Science, Heriot-Watt University, Edinburgh, UK
3 Department of Computer Science and Information Technology, La Trobe University, Melbourne, VIC 3086, Australia

* Corresponding Author: Nayeem Ahmad Khan. Email: email

(This article belongs to this Special Issue: Pervasive Computing and Communication: Challenges, Technologies & Opportunities)

Computers, Materials & Continua 2022, 70(2), 4075-4085. https://doi.org/10.32604/cmc.2022.020389

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.

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Cite This Article

A. Abdullah Alqarni, N. Alsharif, N. Ahmad Khan, L. Georgieva, E. Pardade et al., "Mnn-xss: modular neural network based approach for xss attack detection," Computers, Materials & Continua, vol. 70, no.2, pp. 4075–4085, 2022.



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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