NETPA-DLA: A Deep Learning–Based Network Packet Analyzer for DDoS Detection

Abstract

In the digital era of internetworked systems, understanding, analysing and filtering network traffic is crucial for maintaining security, optimal performance, conducting diagnostic routines and monitoring. This research developed a novel Deep Learning based Network Packet Analyzer (NETPA-DLA) which utilizes an optimal hyperparameter dynamic technique. An ensemble deep learning approach that integrates Deep Belief Networks (DBN), Long Short-Term Memory (LSTM), Recurrent Neural Networks (RNN), Auto-encoders, Transformers and U-Net for robust and accurate classification of distributed denial-of-service (DDoS) was used. The ensembled model was trained on the CIC-DDoS2019 dataset. The study findings contribute to the continuous refinement and deployment of advanced measures to strengthen digital infrastructure against evolving threats. The experiment on the non-pretrained DBN model proved to be better than the pretrained counterpart for DDoS detection, with an accuracy of 99.72 % and false positives of 37 and false negatives of 13 on the validation dataset, with results for all metrics for the LSTM model at 0.9998, the least being validation specificity at 0.9855. Transformer had the highest accuracy level of 0.9998, closely followed by Autoencoder, which had an accuracy level of 0.9986, and ensemble weighted voting at 0.9984, while the RNN obtained a perfect score of 1.0000 for both Recall and Sensitivity across the three relative weights for each of the models. The study shows that DBN can accurately detect and predict DDoS while maintaining the security of the system and given access to the necessary user of the system without any form of denial.