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Our network infrastructure is exposed to persistent threats of DDoS and many unknown attacks. These threats threaten the availability of ISP's network and services. This paper proposes network-based anomalous traffic detection method and presents an anomalous traffic detection system, its architecture and main function blocks. Every five minutes, traffic information and security events are gathered...
The idea of using entropy measurement to detect anomalies is not a novelty in the research community. But all these entropy-based approaches are single-scale based ??complexity?? methods, and don??t consider temporal and spatial correlation in network traffic. In this paper, multi-scale entropy (MSE) and Renyi cross entropy are introduced to solve these problems. First, a kind of Port-to-Port traffic...
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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