Earticle

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

키워드

저자

• Longy O. Anyanwu [ Dept. of Math and Computer Science, Fort Hays State University, Kansa, USA ]
• Jared Keengwe [ Dept. of Teaching and Learning, University of North Dakota, North Dakota, USA ]
• Gladys A. Arome [ College of Educ., Ldrshp, & Tech., Valdosta State University, Georgia, USA ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605