Intrusion Detection using Convolutional Neural Networks with Feature Reduction

The security issues in web application increasing rapidly. Attackers using web application gather the confidential data due to which users suffer from financial as well as identity loss. The hackers on the web applications mainly do denial of service attack and distributed denial of attacks. The goal of this proposed model is intrusion detection in web applications using the convolutional neural network. The convolutional neural network used as classifier and data set is NSL KDD. The 5-fold cross validation mechanism is used for validating the model. The classification result of the model is better due to which accuracy enhanced. The analysis of different classifier is also described in this work.