Unsupervised traffic classification using flow statistical properties and IP packet payload
نویسندگان
چکیده
منابع مشابه
Self-Learning IP Traffic Classification Based on Statistical Flow Characteristics
A number of key areas in IP network engineering, management and surveillance greatly benefit from the ability to dynamically identify traffic flows according to the applications responsible for their creation. Currently such classifications rely on selected packet header fields (e.g. destination port) or application layer protocol decoding. These methods have a number of shortfalls e.g. many ap...
متن کاملIntelligent IP Traffic / Flow Classification System
In QoS-aware networks, such as DiffServ-enabled IP networks, UMTS, or IEEE 802.11e, the QoS-aware applications that run over them can identify service classes to their flows. The flows are then treated by the networks differently with respect to their classes. In contrast, legacy applications are not aware of the concept of QoS and do not specify any classes to their flows. Thus they cannot ben...
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Traffic classification is a very important mathematical and statistical tool in communications and computer networking, which is used to find average and statistical information of the traffic passing through certain pipe or hub. The results achieved from a proper deployment of a traffic analysis method provide valuable insights, including: how busy a link is, the average end-toend delays, and ...
متن کاملModeling IP traffic: joint characterization of packet arrivals and packet sizes using BMAPs
This paper proposes a traffic model and a parameter fitting procedure that are capable of achieving accurate prediction of the queuing behavior for IP traffic exhibiting long-range dependence. The modeling process is a discrete-time batch Markovian arrival process (dBMAP) that jointly characterizes the packet arrival process and the packet size distribution. In the proposed dBMAP, packet arriva...
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ژورنال
عنوان ژورنال: Journal of Computer and System Sciences
سال: 2013
ISSN: 0022-0000
DOI: 10.1016/j.jcss.2012.11.004