EESNN: Hybrid Deep Learning Empowered Spatial-Temporal Features for Network Intrusion Detection System

Intrusion detection systems (IDS) are crucial to network security by identifying and stopping harmful actions. The network intrusion data are blended into an enormous quantity of typical instances as an outcome of the dynamic and time-varying networking surroundings. This leads to a lack of instance...

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Veröffentlicht in:	IEEE access 2024, Vol.12, p.1-1
Hauptverfasser:	Saikam, Jalaiah, Ch, Koteswararao
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Data mining Deep Convolutional Generative Adversarial Networks (DCGANs) Deep learning DenseNet 169 Difficult Set Sampling Technique (DSSTE) algorithm False alarms Feature extraction Generative adversarial networks Intrusion detection system (IDS) Intrusion detection systems Machine learning Network intrusion detection Neural networks Random forests Sampling methods Self Attention based Transformer (SAT-Net) Telecommunication traffic Training
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