원문정보
보안공학연구지원센터(IJMUE)
International Journal of Multimedia and Ubiquitous Engineering
Vol.5 No.3
2010.07
pp.15-22
피인용수 : 0건 (자료제공 : 네이버학술정보)
초록
영어
The ever-growing use of the Internet comes with a surging escalation of communication and data access. Most existing intrusion detection systems have assumed the one -size-fits-all solution model. Such IDS is not as economically sustainable for all organizations. Furthermore, studies have found that Recurrent Neural Network out-performs Feed-forward Neural Network, and Elman Network. This paper, therefore, proposes a scalable application-based model for detecting attacks in a communication network using recurrent neural network architecture. Its suitability for online real-time applications and its ability to self-adjust to changes in its input environment cannot be over-emphasized.
목차
Abstract
1. Introduction
2. SVM with clustering for training
2.1 Clustering tree based on SVM, CT-SVM
2.2 Feed-forward neural networks
2.3 Elman recurrent neural networks
2.4 Recurrent Neural Networks (RNN)
2.5 Real-time recurrent learning algorithm
3. Characteristic features of the proposed system
4. Conclusion
5. Reference
1. Introduction
2. SVM with clustering for training
2.1 Clustering tree based on SVM, CT-SVM
2.2 Feed-forward neural networks
2.3 Elman recurrent neural networks
2.4 Recurrent Neural Networks (RNN)
2.5 Real-time recurrent learning algorithm
3. Characteristic features of the proposed system
4. Conclusion
5. Reference
저자정보
참고문헌
자료제공 : 네이버학술정보