Real-Time Innovation in Future Generation Security for Data Privacy Intrusion System in Machine Learning Approach-Reference-Cited by-同舟云学术

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Real-Time Innovation in Future Generation Security for Data Privacy Intrusion System in Machine Learning Approach

Published:2023-09-14 Issue: Volume: Page:1416-1421
ISSN:
Container-title:2023 6th International Conference on Contemporary Computing and Informatics (IC3I)
language:
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Author:

Wankhede Hansaraj Shalikram¹,Venkatesh R.²,Pundr Sumit³,Madala Ravikiran⁴,Babu Kamunuri Ganapathi⁵,Mallaiah Badepally⁶

Affiliation:

1. G H Raisoni college of Engineering,Department of artificial Intelligence,Nahpur,Maharashtra,India

2. Karpagam College of Engineering,Department of Computer Science and Engineering,Coimbatore,Tamilnadu,India

3. Graphic Era Deemed to be University,Department of Computer Science and Engineering,Dehradun,Uttrakhand,India

4. University of Cumberlands,Durham,North Carolina,US.

5. St. Martin's Engineering College,Department of Computer Science and Engineering,Secunderabad,Telangana,India

6. CVR College of Engineering,Department of Information Technology,Hyderabad,Telangana,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10397581/10397594/10397738.pdf?arnumber=10397738

Reference19 articles.

1. A multiple-kernel clustering based intrusion detection scheme for 5G and IoT networks

2. Real Time Self-developing Cybersecurity Function for 5G

3. Survey of intrusion detection systems: techniques, datasets and challenges

4. Effective Cyber Security Using IoT to Prevent E-Threats and Hacking During Covid-19

5. Machine Learning Based Predicting the Assisted Living Care Needs

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