NDLI: Machine learning based internet traffic recognition with statistical approach

Content Provider	IEEE Xplore Digital Library
Author	Jaiswal, R.C. Lokhande, S.D.
Copyright Year	2013
Description	Author affiliation: Dept. of Electron. & Telecommun., Sinhgad Coll. of Eng., Pune, India (Lokhande, S.D.) \|\| Dept. of Electron. & Telecommun., Pune Inst. of Comput. Technol., Pune, India (Jaiswal, R.C.)
Abstract	The researchers have started looking for Internet traffic recognition techniques that are independent of `well known' TCP or UDP port numbers, or interpreting the contents of packet payloads. Newer approaches classify traffic by recognizing statistical patterns in externally observable attributes of the traffic (such as typical packet lengths and inter-arrival times). The main goal is to cluster or classify the Internet traffic flows into groups that have identical statistical properties. The need to deal with Traffic patterns, large datasets and Multidimensional spaces of flow and packet attributes is one of the reasons for the introduction of Machine Learning (ML) techniques in this field. ML techniques are subset of Artificial Intelligence used for traffic recognition. Further, there are four types of Machine Learning, i.e. Classification (Supervised learning), clustering (Un-Supervised learning), Numeric prediction and Association. In this research paper IP traffic recognition through classification process is implemented. Different researchers are calling this process as IP traffic Recognition, IP traffic Identification, and sometimes IP traffic classification. Here Real time internet traffic has been captured using packet capturing tool and datasets has been developed. Also few standard datasets have been used in this research work. Then using standard attribute selection algorithms, a reduced statistical feature dataset has been developed. After that, Six ML algorithms AdaboostM1, C4.5, Random Forest tree, MLP, RBF and SVM with Polykernel function classifiers are used for IP traffic classification. This implementation and analysis shows that Tree based algorithms are effective ML techniques for Internet traffic classification with accuracy up to of 99.7616 %.
Starting Page	1
Ending Page	6
File Size	1773543
Page Count	6
File Format	PDF
e-ISBN	9781479922758
DOI	10.1109/INDCON.2013.6726074
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2013-12-13
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	C4.5 Random Forest tree Classification algorithms Machine Learning Training Support vector machines MLP Accuracy RBF and SVM with Polykernel function Vegetation AdaboostM1 Internet IP networks Internet Traffic Classification
Content Type	Text
Resource Type	Article

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