(university of bordj bou arreridj, 2024) HEDJAM Lidia; BELOUAHRI Aya
The anomaly in network traffic is a crucial issue that can cause significant losses
in network security and performance. This prompted us to undertake this work to detect
these anomalies accurately and promptly using deep learning techniques.
This thesis investigates the use of long short-term memory (LSTM) neural
networks, one of the deep learning methods, to detect anomalies in network data flows.
LSTMs are well suited to this task thanks to their ability to capture long-term temporal
dependencies. Our approach is distinguished by its ability to detect complex and varied
anomalies, thus improving the security and efficiency of computer networks. The results
show a significant improvement over traditional methods
