Earticle

초록

영어

This study presents an optimized approach for intrusion detection leveraging recurrent neural network on network traffic datasets. We preprocess the data to handle temporal dynamics and design recurrent neural network architectures tailored to the dataset characteristics. We incorporate long short term memory layers for temporal modeling and implement dropout regularization and other optimization techniques to expedite training. Our optimized recurrent neural network consistently outperform other neural network architectures, particularly in internet of things and traditional network environments.

목차

Abstract
1. Introduction
2. Related works
3. Methods
3.1. Dataset
3.2. Experiment setup
4. Experiment result
5. Conclusions
Acknowledgement
References

키워드

저자

• Maira Khalid [ Dept. of AI Convergence Network Ajou University ]
• Laura Tileutay [ Dept. of AI Convergence Network Ajou University ]
• Byeong-hee Roh [ Dept. of AI Convergence Network Ajou University ] Corresponding Author
• Young-Bae Ko [ Dept. of AI Convergence Network Ajou University ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004