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...
Αποθηκεύτηκε σε:
| Κύριος συγγραφέας: | |
|---|---|
| Μορφή: | Thesis |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING MILITARY INSTITUTE OF SCIENCE AND TECHNOLOGY
2019
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| Διαθέσιμο Online: | http://hdl.handle.net/123456789/394 |
| Ετικέτες: |
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Παρόμοια τεκμήρια: FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD
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