Abstract
During the last decade, Internet traffic classification finds its importance not only to safeguard the integrity and security of network resources, but also to ensure the quality of service for business critical applications by optimizing existing network resources. But optimization at first place requires correct identification of different traffic flows. In this paper, we have suggested a framework based on Hidden Markov Model, which will use Internet Packet intrinsic statistical characteristics for traffic classification. The packet inspection based on statistical analysis of its different characteristics has helped to reduce overall computational complexity. Generally, the major challenges associated with any internet traffic classifier are: 1) the limitation to accurately identify encrypted traffic when classification is performed using traditional port based techniques; 2) overall computational complexity, and 3) to achieve high accuracy in traffic identification. Our methodology takes advantage of internet packet statistical characteristics in terms of its size and their inter arrival time in order to model different traffic flows. For experimental results, the data set of mostly used internet applications was used. The proposed HMM models best fit the observed traffic with high accuracy. Achieved traffic identification accuracy was 91% for packet size classifier whereas it was 82% for inter packet time based classifier.
Highlights
Rapid developments in multimedia and broadband applications have made traffic classification a difficult subject, but over the years it has drawn significant importance [1]-[5] among researchers
The researchers have responded to this difficulty by working out different methods of internet traffic classification based on application level usage patterns and customer behavior
Traffic classification is key to network security solution and management architectures [22], [23]
Summary
Rapid developments in multimedia and broadband applications have made traffic classification a difficult subject, but over the years it has drawn significant importance [1]-[5] among researchers. Traditional port based classification techniques are not reliable and cannot identify encrypted traffic. Statistical analysis based deep packet inspection approaches have proven to be more robust and efficient to handle encrypted traffic, which have made it a fertile research area. The debate for optimal technique for traffic classification is still open and with the emergence of new multimedia broadband applications, like Peer to Peer, Internet Protocol based Television and online Games, it has become very difficult for traditional classifiers to identify different traffic flows [7], [8]. The researchers have responded to this difficulty by working out different methods of internet traffic classification based on application level usage patterns and customer behavior. The overall behavior for this model was observed for different traffic flows in various network architectures
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