Idris, Mohd. Yazid and Abdullah, Abdul Hanan and Maarof, Mohd. Aizaini (2006) A synthetic network traffic generator tool for normal and anomaly traffic model. In: Proc. Postgraduate Annual Research Seminar 2006 (PARS 2006) , 2006, UTM.
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Official URL: http://comp.utm.my/pars/files/2013/04/A-Synthetic-...
This research focuses on the development of a synthetic traffic data generator implementing two different statistical models identified as long-range and shortrange dependence. Normal network traffic shows the long-range dependence feature by its traffic burstiness on different time scales. It is believed that anomalous traffic such as worm or denial-of-service can change the distribution to short-range dependence. In order to model long-range dependence of normal network traffic, Fractional Gaussian Noise (FGN) with the Hurst parameter greater than 0.5 has been implemented, while anomalous traffic is represented by FGN with Hurst parameter less than or equal to 0.5. In addition, anomalous traffic has also been modeled by an Auto- Regressive model (AR). As a result, a synthetic network traffic data generator tool has been developed where the network traffic data can be generated easily based on pre-determined parameters.
|Item Type:||Conference or Workshop Item (Paper)|
|Uncontrolled Keywords:||network traffic model, synthetic network traffic data generator, long-range dependence, short-range dependence|
|Subjects:||Q Science > QA Mathematics|
|Divisions:||Computer Science and Information System (Formerly known)|
|Deposited By:||Liza Porijo|
|Deposited On:||24 Apr 2012 01:15|
|Last Modified:||07 Feb 2017 07:24|
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