Multi-scale Memory Residual Network Based Deep Learning Model for Network Traffic Anomaly Detection

M. Jayakrishna; V. Selvakumar; Atul Kumar; Mangesh D. Salunke; Renato R. Maaliw

doi:10.1007/978-981-99-3177-4_35

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Multi-scale Memory Residual Network Based Deep Learning Model for Network Traffic Anomaly Detection

M. Jayakrishna V. Selvakumar Atul Kumar Mangesh D. Salunke Renato R. Maaliw

Year: 2023 Lecture notes in networks and systems Pages: 475-482 Publisher: Springer International Publishing

DOI: 10.1007/978-981-99-3177-4_35

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Keywords:

Residual Computer science Deep learning Artificial intelligence Traffic classification Data pre-processing Network model Anomaly detection Generalization Network architecture Network traffic simulation Data mining Machine learning Network traffic control Algorithm Mathematics

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Anomaly Detection Techniques and Applications

Physical Sciences → Computer Science → Artificial Intelligence

Network Security and Intrusion Detection

Physical Sciences → Computer Science → Computer Networks and Communications

Internet Traffic Analysis and Secure E-voting

Physical Sciences → Computer Science → Artificial Intelligence

Multi-scale Memory Residual Network Based Deep Learning Model for Network Traffic Anomaly Detection

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