Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/76517
Title: A feasibility study of stateful automaton packet inspection for streaming application detection systems
Authors: Tseng, KK
Lo, J
Liu, YM
Chang, SH
Merabti, M
Ng, FCK 
Wu, CH 
Keywords: Firewall
Packet classification
Stateful automaton
Aho-Corasick
Issue Date: 2017
Publisher: Taylor & Francis
Source: Enterprise information systems, 2017, v. 11, no. 9, p. 1317-1336 How to cite?
Journal: Enterprise information systems 
Abstract: The rapid development of the internet has brought huge benefits and social impacts; however, internet security has also become a great problem for users, since traditional approaches to packet classification cannot achieve satisfactory detection performance due to their low accuracy and efficiency. In this paper, a new stateful packet inspection method is introduced, which can be embedded in the network gateway and used by a streaming application detection system. This new detection method leverages the inexact automaton approach, using part of the header field and part of the application layer data of a packet. Based on this approach, an advanced detection system is proposed for streaming applications. The workflow of the system involves two stages: the training stage and the detection stage. In the training stage, the system initially captures characteristic patterns from a set of application packet flows. After this training is completed, the detection stage allows the user to detect the target application by capturing new application flows. This new detection approach is also evaluated using experimental analysis; the results of this analysis show that this new approach not only simplifies the management of the state detection system, but also improves the accuracy of data flow detection, making it feasible for real-world network applications.
URI: http://hdl.handle.net/10397/76517
ISSN: 1751-7575
EISSN: 1751-7583
DOI: 10.1080/17517575.2016.1234070
Appears in Collections:Journal/Magazine Article

Access
View full-text via PolyU eLinks SFX Query
Show full item record

Page view(s)

3
Citations as of May 21, 2018

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.