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

作     者：Ahmed Abdullah Alqarni Nizar Alsharif Nayeem Ahmad Khan Lilia Georgieva Eric Pardade Mohammed Y.Alzahrani 

作者机构：Department of Computer Sciences and Information TechnologyAlBaha UniversityAlBahaSaudi Arabia Department of Computer ScienceHeriot-Watt UniversityEdinburghUK Department of Computer Science and Information TechnologyLa Trobe UniversityMelbourneVIC 3086Australia 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第70卷第2期

页      面：4075-4085页

核心收录：

学科分类：08[工学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0801[工学-力学（可授工学、理学学位）] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Cybersecurity XSS deep learning modular neural network 

摘      要：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 *** recent years,researchers have used different machine learning methods to detect network attacks without relying on those *** methods generally have a high false-positive rate which is not adequate for an industry-ready intrusion detection *** 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 *** were performed using a dataset consists of 1000 malicious and 10000 benign *** model uses 50 features selected by using Pearson correlation method and will be used in the detection and preventions of XSS *** results obtained from the experiments depict improvement in the detection accuracy as high as 99.96%compared to other approaches.