Detecting Cross-Site Scripting Attacks with Modified Neural Networks

Published on August 29, 2022

Imagine you’re a superhero protecting a city from sneaky villains. One particular villain, the Cross-Site Scripting Attack (XSS), poses a serious threat to the city’s network security. To stop these sneak attacks, scientists have developed a powerful weapon called the Modified ResNet Block and NiN Model Convolutional Neural Network (MRBN-CNN). This superhero model preprocesses URLs like detectives analyzing clues, extracts features from multiple perspectives, and utilizes its 1*1 convolution superpower to replace traditional methods. When put to the test against other detection models, MRBN-CNN outperforms them all in terms of accuracy and convergence time. The results are astounding! Compared to a baseline, this superhero model achieves an impressive 99.23% accuracy, 99.94% precision, and a recall value of 98.53%. With MRBN-CNN on our side, we can finally sleep soundly at night knowing our networks are protected from XSS villains. Read the full research article to uncover the superpowers of this amazing model!

Cross-site scripting (XSS) attacks are currently one of the most threatening network attack methods. Effectively detecting and intercepting XSS attacks is an important research topic in the network security field. This manuscript proposes a convolutional neural network based on a modified ResNet block and NiN model (MRBN-CNN) to address this problem. The main innovations of this model are to preprocess the URL according to the syntax and semantic characteristics of XSS attack script encoding, improve the ResNet residual module, extract features from three different angles, and replace the full connection layer in combination with the 1*1 convolution characteristics. Compared with the traditional machine learning and deep learning detection models, it is found that this model has better performance and convergence time. In addition, the proposed method has a detection rate compared to a baseline of approximately 75% of up to 99.23% accuracy, 99.94 precision, and a 98.53% recall value.

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