Web intrusion detection using character level machine learning approaches with upsampled data

Today, people fulfill their needs in many areas such as shopping, health, and finance online. Besides many well-meaning people who use websites for their own needs, there are also people who send attack requests to get these people's personal data, get website owners' information, and damage the application. The attack types such as SQL injection and XSS can seriously harm web applications and users. Detecting these cyber-attacks manually is very time-consuming and difficult to adapt to new attack types. Our proposed study performs attack detection using different machine learning and deep learning approaches with a larger dataset obtained by combining CSIC 2012 and ECML/PKDD datasets. In this study, we evaluated our classification results which experimented with different algorithms based on computation time and accuracy. In addition to applying different algorithms, experiments on various learning models were applied with our data upsample method for balancing the dataset labels. As a result of the binary classification, LSTM achieves the best result in terms of accuracy, and a positive effect of the upsampled data on accuracy has been observed. LightGBM was the algorithm with the highest performance in terms of computation time.

---

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).