Abstract
Nowadays, the increased use of Web services has resulted in a rise in the danger of web application attacks. These types of attacks are become more severe by the day. Intrusion detection systems (IDS) are essential in proactively detecting Web threats. The network traffic contains a significant number of characteristics. Identification of irrelevant and essential features is a primary task in machine learning. Pre-processing, feature selection, and naive Bayes (NB) classifier proposed in this study as part of IDS for Web application attack. The system is evaluated on the CICIDS-2017 dataset and employs a random forest to select significant features for cross-site scripting (XSS) attack classification. Experimental results show that the feature selection using random forest enhanced the classifier's performance in terms of accuracy, detection rate and execution time, are also reducing false alarms.in comparison with using all features in the classification method.
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