Earticle

초록

영어

With the development of technology, wireless sensor networks(WSNs) has been widely used in military, political, medical and other fields, their characteristics of data-centric become increasingly prominent. In this paper, a data-oriented intruding detection method based on chaos theoy is proposed. We use the theory of chaotic system to analyze the internal rules of the sensory data and predict the data by RBF neural network firstly, then make an initial detection of false injected data attack according to whether the difference between the predicted and actual value is more than the threshold, finally confirming the attack by checking whether the number of abnormal within the cycle lies in the corresponding range. Experimental results show that RBF neural network predict sensory data more accurate, our approach can effectively distinguish the abnormal events caused by the attack or environmental factors and has high intrusion detection accuracy.

목차

Abstract
 1. Introduction
 2. RelatedWork
 3. System Model
  3.1 WSNs Model
  3.2 Data Preprocessing
  3.3 Judgement of Chaotic System and Making Prediction
  3.4. Detection Algorithm
 4. Simulation Results
  4.1 Experimental Environment
  4.2 Results and Analysis
 References

키워드

저자

• Xinling Kong [ School of Computer Science and Technology, Huaqiao University, Xiamen, China ] Corresponding Author
• Yonghong Chen [ School of Computer Science and Technology, Huaqiao University, Xiamen, China ]
• HuiTian [ School of Computer Science and Technology, Huaqiao University, Xiamen, China ]
• Tian Wang [ School of Computer Science and Technology, Huaqiao University, Xiamen, China ]
• Yiqiao Cai [ School of Computer Science and Technology, Huaqiao University, Xiamen, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Security and Its Applications

• 간기

  격월간

• pISSN

  1738-9976

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605