Deep Learning-Powered Intrusion Detection Systems: Enhancing Efficiency in Network Security-Reference-Cited by-同舟云学术

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Deep Learning-Powered Intrusion Detection Systems: Enhancing Efficiency in Network Security

Published:2024-05-09 Issue: Volume: Page:1-7
ISSN:
Container-title:2024 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI)
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Author:

Balamurugan M.¹,Varanasi UshaBala²,Mangai R.Alarmelu³,Vinayagam P⁴,Karuppaiah S⁵,Sayyed Hifajatali⁶

Affiliation:

1. Saveetha Institute of Medical and Technical Sciences,Saveetha School of Engineering,Department of ECE,Chennai,Tamil Nadu

2. ANITS Engineering College,Department of CSE,Vizag,Andhra Pradesh,India

3. Dr.SNSRajalakshmi College of Arts and Science,Department of Computer Science,Coimbatore,Tamil Nadu,India

4. Saveetha Engineering College,Department of ECE,Chennai,Tamil Nadu

5. R.M.D Engineering College,Department of ECE,Thiruvallur,Tamil Nadu,India

6. Symbiosis International Deemed University,Symbiosis Law School Hyderabad,Pune,India

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10601631/10601667/10602010.pdf?arnumber=10602010

Reference16 articles.

1. XTM: A Novel Transformer and LSTM-Based Model for Detection and Localization of Formally Verified FDI Attack in Smart Grid

2. False data injection threats in active distribution systems: A comprehensive survey

3. FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination

4. A Secure Federated Learning Framework for Residential Short Term Load Forecasting

5. Intrusion Detection in SCADA Based Power Grids: Recursive Feature Elimination Model With Majority Vote Ensemble Algorithm

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