A robust Artificial Intelligence based Software Modelling Tool for automatically detecting and characterizing web attacks

Abstract

Online applications are frequently vulnerable and network accessible over the internet. Web applications are appealing targets in the eyes of cyber attackers. The most frequent types of attacks that can disable online services include SQL injection, Cross-Site Scripting (XSS), Database Attacks, Dynamic Code Execution (DCE), and Remote Code Execution (RCE). These attacks can cause significant financial losses for service providers and clients. To understand the execution behaviour of a web application, these attacks should be tracked and automatically characterised. A new, robust Artificial Intelligence (AI) based Software Modelling Tool for automatically detecting and characterising web attacks is provided by the proposed work, which is described here. It uses Long Short-Term Memory (LSTM) to detect web attacks and includes record traces, datasets, sampling, training data, test data, LSTM models, threshold, and classification and model evaluation. The suggested study makes use of the Long Short-Term Memory (LSTM) Model to raise the Software Modeling Tool’s prediction accuracy. Support Vector Machine (SVM), Naive Bayes, and Autoencoder Deep Learning algorithms are examples of prior art that are contrasted with the principal implementations of the present invention. The Long Short-Term Memory (LSTM) Model transforms unlabeled data into deep learning features without the need for labelled input data during model training. The proposed work has a Fscore of 90.49%, Accuracy of 97.61%, Precision of 90%, Recall of 90.99%. The dataset utilised in the current disclosure has a total of 666 records, of which 532 records are used to train the AI-based Software Modelling Tool and 134 records to test it.