Deep learning approach for Network Intrusion Detection in Software Defined Networking

Proceedings - 2016 International Conference on Wireless Networks and Mobile Communications, WINCOM 2016: Green Communications and Networking

Volumn , Issue , 2016, Pages 258-263

  Tang, Tuan A ^a   Mhamdi, Lotfi ^a   McLernon, Des ^a   Zaidi, Syed Ali Raza ^a   Ghogho, Mounir ^a,b  

^a UNIVERSITY OF LEEDS   (United Kingdom)

^b INTERNATIONAL UNIVERSITY OF RABAT   (Morocco)

Author keywords

deep learning; intrusion detection; network security; SDN; software defined networking

Indexed keywords

MERCURY (METAL); MOBILE TELECOMMUNICATION SYSTEMS; NETWORK SECURITY; SIGNAL DETECTION; SOFTWARE DEFINED NETWORKING; WIRELESS NETWORKS;

ANOMALY DETECTION; DEEP LEARNING; DEEP NEURAL NETWORKS; GLOBAL NETWORKS; INTRUSION DETECTION SYSTEMS; NETWORK INTRUSION DETECTION; POTENTIAL THREATS; SOFTWARE DEFINED NETWORKING (SDN);

INTRUSION DETECTION;

EID: 85010301146     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/WINCOM.2016.7777224     Document Type: Conference Paper

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