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Detection and defending the XSS attack using novel hybrid stacking ensemble learning-based DNN approach

Digital Communications and Networks 2024年第3期10卷 716-727页

作者： Muralitharan Krishnan Yongdo Lim Seethalakshmi Perumal gayathri palanisamy IMS Sungkyunkwan UniversitySuwon16419South Korea Department of Mathematics Sungkyunkwan UniversitySuwon16419South Korea Department of Information Technology Madras Institute of TechnologyChennaiIndia Department of Mathematics PSG College of Arts and ScienceTamilnaduIndia

Existing web-based security applications have failed in many situations due to the great intelligence of *** web applications,Cross-Site Scripting(XSS)is one of the dangerous assaults experienced while modifying an organization's or user's *** avoid these security challenges,this article proposes a novel,all-encompassing combination of machine learning(NB,SVM,k-NN)and deep learning(RNN,CNN,LSTM)frameworks for detecting and defending against XSS attacks with high accuracy and *** on the representation,a novel idea for merging stacking ensemble with web applications,termed“hybrid stacking”,is *** order to implement the aforementioned methods,four distinct datasets,each of which contains both safe and unsafe content,are *** hybrid detection method can adaptively identify the attacks from the URL,and the defense mechanism inherits the advantages of URL encoding with dictionary-based mapping to improve prediction accuracy,accelerate the training process,and effectively remove the unsafe JScript/JavaScript keywords from the *** simulation results show that the proposed hybrid model is more efficient than the existing detection *** produces more than 99.5%accurate XSS attack classification results(accuracy,precision,recall,f1_score,and Receiver Operating Characteristic(ROC))and is highly resistant to XSS *** order to ensure the security of the server's information,the proposed hybrid approach is demonstrated in a real-time environment.

关键词： Machine learning Deep neural networks Classification Stacking ensemble XSS attack URL encoding JScript/JavaScript Web security

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