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The work presents results of a numerical study of fractal characteristics of multifractal stream at addition of stream, which does not have multifractal properties. They showed that the generalized Hurst exponent of total stream tends to one of original multifractal stream with increase in signal/noise ratio.
In this paper, the real network traffic is analyzed about its chaotic dynamic properties and the traffic signals are reconstructed using FBM based fractal interpolation algorithm. The self-similarity of network traffic is analyzed by estimating the value of Hurst exponent and the traffic time series is reconstructed as a phase trajectory by properly choosing some parameters. Through the reconstructed...
The self-similar nature of bursty Internet traffic has been investigated for the last decade. A first generation of papers, approximately from 1994 to 2004, argued that the traditionally used Poisson models oversimplified the characteristics of network traffic and were not appropriate for modeling bursty, local-area, and wide-area network traffic. Since 2004, a second generation of papers has challenged...
In this paper, we investigate temporal and spatial correlations of time series of unwanted traffic (i.e., darknet or network telescope traffic) in order to estimate statistical behavior of unwanted activities from a small size of darknet address block. First, from the analysis of long-range dependency, we point out that TCP time series has a weak temporal correlation though UDP time series without...
In contrast to many techniques exploiting temporal patterns of traffic from a single network element, network-wide traffic analysis mainly focuses on the spatial behavior across the whole network. This paper proposes a spatial hidden Markov model (SHMM) to learn the normal patterns of network-wide traffic. Combined with topology information, SHMM models traffic volumes on links as probabilistic outputs...
The main drawback of traditional intrusion detection systems makes anomaly detection systems an active research area. In this paper we introduce a novel network-based anomaly detection approach using stochastic learning automata. The paper main objective is to construct a network-based statistical anomaly detection system capable of classifying the ensemble network broadcast traffic as normal or abnormal...
To detect the anomalous events in the time series we propose a new idea that we can view the time series of traffic flows as a nonstationary Poisson process associated with superstatistics theory. According to the superstatistics theory, the complex dynamic system may have a large fluctuationary of intensive quantities on large time scales which causes the system to behave as nonstationarity and nonlinearity...
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