원문정보
한국인터넷방송통신학회
The International Journal of Advanced Smart Convergence
Volume 11 Number 4
2022.12
pp.10-19
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
AI-based Network Intrusion Detection Systems (AI-NIDS) detect network attacks using machine learning and deep learning models. Recently, unsupervised AI-NIDS methods are getting more attention since there is no need for labeling, which is crucial for building practical NIDS systems. This paper aims to test the impact of designing autoencoder models that can be applied to unsupervised an AI-NIDS in real network systems. We collected security events of legacy network security system and carried out an experiment. We report the results and discuss the findings
목차
Abstract
1. Introduction
2. Unsupervised Learning Models
3. Experiment Design
3.1 Security Event Dataset
3.2 Experiment Setup
3.3 Evaluation Metrics
4. The Experiment Result
4.1 Attack Detection Performance
4.2 Data Distribution
4.3 Discussion
5. Conclusion
Acknowledgement
References
1. Introduction
2. Unsupervised Learning Models
3. Experiment Design
3.1 Security Event Dataset
3.2 Experiment Setup
3.3 Evaluation Metrics
4. The Experiment Result
4.1 Attack Detection Performance
4.2 Data Distribution
4.3 Discussion
5. Conclusion
Acknowledgement
References
저자정보
참고문헌
자료제공 : 네이버학술정보