A Contribution to DDoS Attack Detection Based on Deep Neural Networks
Bianca Badidová, Radoslav Forgáč, Miloš Očkay, Martin Javurek · 2023
This work deals with the implementation of Bidirectional Long Short-Term Memory and Gated Recurrent Unit neural networks to detect anomalies in the form of DDoS attacks in the CIC-DDoS2019 dataset. The proposed approach is based on the regression models of the mentioned neural networks, which generate a certain reconstruction error for the samples fed into their input. The intention was to achieve a smaller reconstruction error for the network flow samples not containing a DDoS attack compared to samples containing such an attack. The reconstruction error is evaluated using a threshold to decide whether an attack is present. The result of this work is a comparison of two validated recurrent neural network models in network traffic anomaly detection for the CIC-DDoS2019 dataset. The comparison was performed using commonly used neural network evaluation metrics.