Automatic Payload Signature Update System for the Classification of Dynamically Changing Internet Applications
- Authors
- Shim, Kyu-Seok; Goo, Young-Hoon; Lee, Dongcheul; Kim, Myung-Sup
- Issue Date
- 31-3월-2019
- Publisher
- KSII-KOR SOC INTERNET INFORMATION
- Keywords
- Automatic; Traffic Classification; Association Rule Mining; Payload Signature
- Citation
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, v.13, no.3, pp.1284 - 1297
- Indexed
- SCIE
SCOPUS
KCI
- Journal Title
- KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
- Volume
- 13
- Number
- 3
- Start Page
- 1284
- End Page
- 1297
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/66587
- DOI
- 10.3837/tiis.2019.03.009
- ISSN
- 1976-7277
- Abstract
- The network environment is presently becoming very increased. Accordingly, the study of traffic classification for network management is becoming difficult. Automatic signature extraction system is a hot topic in the field of traffic classification research. However, existing automatic payload signature generation systems suffer problems such as semi-automatic system, generating of disposable signatures, generating of false-positive signatures and signatures are not kept up to date. Therefore, we provide a fully automatic signature update system that automatically performs all the processes, such as traffic collection, signature generation, signature management and signature verification. The step of traffic collection automatically collects ground-truth traffic through the traffic measurement agent (TMA) and traffic management server (TMS). The step of signature management removes unnecessary signatures. The step of signature generation generates new signatures. Finally, the step of signature verification removes the false-positive signatures. The proposed system can solve the problems of existing systems. The result of this system to a campus network showed that, in the case of four applications, high recall values and low false-positive rates can be maintained.
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Collections - Graduate School > Department of Computer and Information Science > 1. Journal Articles
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