FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD
I am precisely thankful to Almighty for his unceasing and immense blessings without which my thesis completion would remain scattered and incomplete. I express my heartiest gratitude, profound indebtedness and deep respect to my supervisor, Dr. Md. Mahbubur Rahman, Professor, Department of CSE, M...
Wedi'i Gadw mewn:
| Prif Awdur: | |
|---|---|
| Fformat: | Traethawd Ymchwil |
| Iaith: | Saesneg |
| Cyhoeddwyd: |
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY
2019
|
| Mynediad Ar-lein: | http://hdl.handle.net/123456789/394 |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
|
Eitemau Tebyg: FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD
- FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD /
- AN ADVERSARIAL APPROACH FOR INTRUSION DETECTION USING DEEP LEARNING /
- DESIGNING SOFTWARE DEFINED RADIO (SDR) FOR SECURED MULTIMODE WIDEBAND OPERATION /
- ENHANCED DETECTION AND ANOMALY IDENTIFICATION IN CHIPLESS RFID SYSTEMS USING SCALABLE MACHINE LEARNING MODELS /
- METAHEURISTIC SEARCH-BASED FEATURE SET SELECTION TECHNIQUES FOR CREDIT CARD FRAUD DETECTION /
- A DEEP LEARNING-BASED APPROACH FOR DETECTING BANGLA SPAM EMAILS /