FLOW BASED ANOMALY DETECTION IN SOFTWARE DEFINED NETWORKING: A DEEP LEARNING APPROACH WITH FEATURE SELECTION METHOD /
Enregistré dans:
| Auteur principal: | |
|---|---|
| Collectivité auteur: | |
| Format: | Livre |
| Langue: | anglais |
| Publié: |
Dhaka :
CSE DEPT MIST ,
c 2018.
|
| Sujets: | |
| Tags: |
Pas de tags, Soyez le premier à ajouter un tag!
|
Documents similaires: 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 /