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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...
Denial of Service (DoS) attacks are ubiquitous to computer networks. Flood based attacks are a common class of DoS attacks. DoS detection mechanisms that aim at detecting floods mainly look for sudden changes in the traffic and mark them anomalous. In this paper, we propose a method that considers the traffic in a network as a time-series and smoothens it using exponential moving average and analyzes...
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