Unsupervised Deep Learning Approaches for Anomaly Detection in IoT Data Streams-Reference-Cited by-同舟云学术

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Unsupervised Deep Learning Approaches for Anomaly Detection in IoT Data Streams

Published:2023-09-20 Issue: Volume: Page:
ISSN:
Container-title:2023 4th International Conference on Smart Electronics and Communication (ICOSEC)
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Author:

Suresh Helina Rajini¹,Neelaveni R.²,Kumar Bonda Kiran³,Arunadevi R.⁴,G Indhumathi⁵,Varun M⁶

Affiliation:

1. Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology,Department of ECE,Chennai,Tamilnadu,India

2. SRM institute of science and technology,Department of CSE,Chennai,Tamilnadu,India

3. KL School of Architecture, KL Deemed to be University,Andhrapradesh,India

4. Prince Shri Venkateshwara Padmavathy Engineering College,Department of Computer Science and Engineering,Chennai,Tamilnadu,India

5. Rajalakshmi Engineering College,Department of ECE,Chennai,Tamilnadu,India

6. Saveetha Engineering College,Department of ECE,Chennai,Tamilnadu,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10275782/10275807/10276180.pdf?arnumber=10276180

Reference15 articles.

1. Deep Learning-Based Anomaly Detection in LAN from Raw Network Traffic Measurement

2. Application of Deep Learning for Crowd Anomaly Detection from Surveillance Videos

3. An Improved Approach for Generative Model-Based Product Image Anomaly Detection

4. Semi-supervised Anomaly Detection with Reinforcement Learning

5. Deep Learning for Network Anomalies Detection

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