Open AccessFRACTAL CHARACTERISTICS OF NETWORK TRAFFIC AND ITS CORRELATION WITH NETWORK SECURITY
Author(s) -
Caichang Ding,
Yiqin Chen,
Zhiyuan Liu,
Ahmed Alshehri,
Tianyin Liu
Publication year - 2022
Publication title -
fractals
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.654
H-Index - 44
eISSN - 1793-6543
pISSN - 0218-348X
DOI - 10.1142/s0218348x22400679
Subject(s) - hurst exponent , denial of service attack , computer science , traffic generation model , wavelet , network security , data mining , multifractal system , fractal , algorithm , artificial intelligence , mathematics , real time computing , computer network , statistics , the internet , mathematical analysis , world wide web
Based on the analysis of the self-similarity of network traffic, a network anomaly detection technology is proposed by combining with the fuzzy logic so as to explore the fractal characteristics of network traffic. The concepts of network traffic and network security are introduced. Then, a network traffic model of network traffic is proposed based on the fractal theory and wavelet analysis. Finally, a distributed denial of service (DDoS) that attacks the monitoring and intensity judgment method is put forward based on the fuzzy logic theory. The results show that the autocorrelation function of the multifractal wavelet model constructed based on the local Hurst exponent (LHE) can reach a mean square error (MSE) of [Formula: see text], which proves that the network traffic model proposed can reduce the impact of the non-stationary characteristics of the network traffic on the modeling accuracy. The network security detection method proposed can monitor the DDoS attacks and can accurately judge the attack intensity in real time. The research in this study provides an important reference for the scientific operation of the network.