A COMPREHENSIVE STUDY OF DEEP LEARNING, TEMPORAL ANALYSIS AND SEMANTIC-ANOMALY SUSION FOR IOT AND IIOT INTRUSION DETECTION /
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| Κύριος συγγραφέας: | |
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| Συγγραφή απο Οργανισμό/Αρχή: | |
| Μορφή: | Βιβλίο |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
Dhaka :
EECE DEPT. MIST,
c2026.
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Παρόμοια τεκμήρια: A COMPREHENSIVE STUDY OF DEEP LEARNING, TEMPORAL ANALYSIS AND SEMANTIC-ANOMALY SUSION FOR IOT AND IIOT INTRUSION DETECTION /
- AN ADVERSARIAL APPROACH FOR INTRUSION DETECTION USING DEEP LEARNING /
- 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 /
- AUTOMATIC HAND GESTURE RECOGNITION USING SEMANTIC SEGMENTATION AND DEEP LEARNING /
- A COMPARATIVE BEHAVIOURAL STUDY OF MACHINE LEARNING AND DEEP LEARNING MODELS FOR NETWORK INTRUSION DETECTION ACROSS DATASETS OF DIFFERING STATISTICAL CHARACTER /
- ENHANCED DETECTION AND ANOMALY IDENTIFICATION IN CHIPLESS RFID SYSTEMS USING SCALABLE MACHINE LEARNING MODELS /