Leveraging Deep Learning for Network Anomaly Detection

Author:

Kourtis Michail-Alexandros1,Oikonomakis Andreas2,Papadopoulos Dimitris3,Xylouris George1,Chochliouros Ioannis P.4

Affiliation:

1. ORION Innovations Private Company,Athens,Greece

2. National Center of Scientific Research “Demokritos”,Athens,Greece

3. Infili Technologies PC,Athens,Greece

4. Hellenic Telecommunications Organization S.A. (OTE),Athens,Greece

Publisher

IEEE

Reference29 articles.

1. CHARISMA: Converged heterogeneous advanced 5G cloud-RAN architecture for intelligent and secure media access

2. A real-time DDoS attack detection and prevention system based on per-IP traffic behavioral analysis;yi;3rd International Conference on Computer Science and Information Technology,2010

3. Modeling A Malware Detection And Categorization System Based On Seven Network Flow- Based Features;sopuru;International Journal of Innovative Technology and Exploring (IJITEE),2019

4. Machine Learning based Traffic Classification using Low Level Features and Statistical Analysis;rajesh;International Journal of Computer Applications,2014

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1. Deep machine learning based Usage Pattern and Application classifier in Network Traffic for Anomaly Detection;2023 International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems (ICAECIS);2023-04-19

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