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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...
Most researches regard the real traffic has self-similarity, so traditional model based Possion or Markov canpsilat adapt to the real traffic. In this paper, the authors use the estimation based to Hurst parameter to detect Dos attack to research on the affect of Hurst parameter change brought by DoS attack. By analyzing the 1998 DARPA Intrusion Detection Evaluation dataset, it is verify that this...
The 3 most important issues for anomaly detection based intrusion detection systems by using data mining methods are: feature selection, data value normalization, and the choice of data mining algorithms. In this paper, we study primarily the feature selection of network traffic and its impact on the detection rates. We use KDD CUP 1999 dataset as the sample for the study. We group the features of...
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...
Summary form only given. For many Internet applications, the ability to protect the identity of participants and the characteristics of their communication in distributed applications is critical. For such applications, a number of traffic camouflaging systems have been developed over the past several years. The effectiveness of these systems relies greatly on (1) the protocol by which messages are...
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