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dc.contributor.authorLu, Chun-Nanen_US
dc.contributor.authorLai, Yuan-Chengen_US
dc.contributor.authorHuang, Chun-Yingen_US
dc.contributor.authorLin, Ying-Daren_US
dc.date.accessioned2018-08-21T05:56:54Z-
dc.date.available2018-08-21T05:56:54Z-
dc.date.issued2016-01-01en_US
dc.identifier.urihttp://hdl.handle.net/11536/146814-
dc.description.abstractSignature matching is commonly used in network traffic classification and can provide accurate and efficient results. However, it requires constant updates of signatures and can't be applied to encrypted traffic. Statistical behavior-based approaches can avoid the drawback of payload encryption. However, the computational complexity of related statistical features may prevent them being deployed in systems that are expected to respond in limited time. In this work, we combine the advantages of statistics-based classification approaches and hardware design techniques to develop a balanced classifier that can provide timely responses to. Two statistics-based solutions, a message size distribution classifier (MSDC) and a message size sequence classifier (MSSC) which depend on classification accuracy and real timeliness arc proposed. The former aims to identify network flows in an accurate but not-so-fast manner, while the latter aims to provide a lightweight and real-time solution. Simulations showed that MSSC contributed 77.4% and MSDC contributed 22.6% of decision rounds. Furthermore, our design can achieve an accuracy of more than 94% while achieving a throughput of 80 Gbps.en_US
dc.language.isoen_USen_US
dc.subjecttraffic classificationen_US
dc.subjectpacket sizeen_US
dc.subjectmessage size distributionen_US
dc.subjectsequenceen_US
dc.subjecthardware classifieren_US
dc.titleHardware Design for Statistical Network Traffic Classifiersen_US
dc.typeProceedings Paperen_US
dc.identifier.journal2016 2ND IEEE INTERNATIONAL CONFERENCE ON COMPUTER AND COMMUNICATIONS (ICCC)en_US
dc.citation.spage2425en_US
dc.citation.epage2429en_US
dc.contributor.department資訊工程學系zh_TW
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.identifier.wosnumberWOS:000411576804020en_US
Appears in Collections:Conferences Paper