Network Intrusion Detection System
Can Tho University of Technology
Jan 2024 - Aug 2024 (8 months)
Spearheaded research on multiple deep learning architectures for network intrusion detection, achieving about 98% detection accuracy on the UNSW-NB15 dataset. Implemented and benchmarked various neural network models (CNN, RNN, Transformer, GRU, LSTM, BiLSTM) to identify optimal architecture for different attack types. Conducted comprehensive performance analysis using key metrics (accuracy, precision, recall, F1- score) to enhance detection capabilities.