Detecting Attacks on Web Applications: Using the Convolutional Neural Network, Multilayer Perceptron, and Random Forest Models
Pavel Sobolev, Igor Vitalievich Kotenko · 2025
Due to the increasing number of attacks on web applications, ensuring the security of web applications is an important area in the field of information security. The article presents a combined approach for detecting attacks on web applications. The approach uses convolutional neural networks combined with multilayer perceptrons to detect single attacks. The random forest is also used to detect attacks on web applications based on a dataset that includes multiple attacks. The random forest classifies attacks based on additional parameters such as method, the content type, connection, length, and content. The test results showed that the presented approach performed better than others when detecting the same type of attacks from Kaggle datasets, and together with the modification using a random forest, it was better than others to identify multiple attacks. The proposed approach can be used to improve the security systems of web applications, which will reduce the risk of successful attacks.