A COMPREHENSIVE STUDY OF DEEP LEARNING, TEMPORAL ANALYSIS AND SEMANTIC-ANOMALY SUSION FOR IOT AND IIOT INTRUSION DETECTION /
Wedi'i Gadw mewn:
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| Awdur Corfforaethol: | |
| Fformat: | Llyfr |
| Iaith: | Saesneg |
| Cyhoeddwyd: |
Dhaka :
EECE DEPT. MIST,
c2026.
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| Pynciau: | |
| Tagiau: |
Dim Tagiau, Byddwch y cyntaf i dagio'r cofnod hwn!
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Eitemau Tebyg: 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 /