Browsing by Author "BELOUAHRI Aya"

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Deep Learning-based Anomaly Detection in Network Traffic Patterns
(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