STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent

STID-Net: Optimizing Intrusion Detection in IoT with Gradient Descent

The rapid evolution of IoT environment in medical and industrial applications has led to an increase in network vulnerabilities, making an intrusion detection system a critical requirement. Existing methods often struggle in capturing complex and irregular patterns from dynamic intrusion data, makin...

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Bibliographic Details
Main Authors: James Deva Koresh Hezekiah, Usha Nandini Duraisamy, Kalaichelvi Nallusamy, Avudaiammal Ramalingam, Saranya Chandran, Murugesan Rajeswari Thiyagupriyadharsan, Periasamy Selvaraju, Rajagopal Maheswar
Format: Article
Language:English
Published: MDPI AG 2025-03-01
Series:Sensors
Subjects:
cybersecurity
anomaly detection
feature optimization
pattern recognition
sequential data analysis
Online Access:https://www.mdpi.com/1424-8220/25/6/1852
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