Comparative Analysis of CNN and Transformers on Malicious Intent Detection in HTTP

The utilization of web-based applications has greatly increased in recent years. This, in turn, has raised security concerns and data breaches leading to losses of millions of dollars. A large part of these attacks is carried out through modifications in HTTP requests and are often too complex to be recognised through simple rule-based checks. Thus, it is vital to utilize machine learning approaches in order to protect the applications against such attack vectors. Additionally, given the pace at which web servers need to operate, these machine learning methods need to be extremely fast at processing the requests. The paper presents a comparison of two recent deep learning models to recognize the web application attacks namely, a convolution neural network (CNN) and a Transformer-based model. We compare the prediction accuracies of both the models as well as their prediction time to identify the best model suited for being deployed in a real-life scenario. Based on implementation and results, it is observed that for our dataset, CNN tends to perform better in terms of both, accuracy and prediction time.