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

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