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Effective behavior signature extraction method using sequence pattern algorithm for traffic identification
Author(s) -
Shim KyuSeok,
Yoon SungHo,
Sija Baraka D.,
Park JunSang,
Cho Kyunghee,
Kim MyungSup
Publication year - 2017
Publication title -
international journal of network management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.373
H-Index - 28
eISSN - 1099-1190
pISSN - 1055-7148
DOI - 10.1002/nem.2011
Subject(s) - computer science , signature (topology) , identification (biology) , data mining , process (computing) , algorithm , sequence (biology) , key (lock) , pattern recognition (psychology) , artificial intelligence , computer security , mathematics , botany , geometry , genetics , biology , operating system
Summary With the rapid development of the internet and a vigorous emergence of new applications, traffic identification has become a key issue. Although various methods have been proposed, there are still several limitations to achieving fine‐grained and application‐level identification. Therefore, we previously proposed a behavior signature model for extracting a unique traffic pattern of an application. Although this signature model achieves a good identification performance, it has trouble with the signature extraction, particularly from a huge amount of input traffic, because a Candidate‐Selection method is used for extracting the signature. To improve this inefficiency in the extraction process, in this paper, we propose a novel behavior signature extraction method using a sequence pattern algorithm. The proposed method can extract a signature regardless of the volume of input traffic because it excludes certain unsatisfactory candidates using a predefined support value during the early stage of the process. We proved experimentally the feasibility of the proposed extraction method for 7 popular applications.