Application traffic classification using payload size sequence signature

2017 ◽  
Vol 27 (5) ◽  
pp. e1981 ◽  
Author(s):  
Kyu-Seok Shim ◽  
Jae-Hyun Ham ◽  
Baraka D. Sija ◽  
Myung-Sup Kim
Keyword(s):  
Traffic Classification ◽  
Sequence Signature

scholarly journals Common Knowledge Based and One-Shot Learning Enabled Multi-Task Traffic Classification

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 39485-39495 ◽  
Author(s):  
Haifeng Sun ◽  
Yunming Xiao ◽  
Jing Wang ◽  
Jingyu Wang ◽  
Qi Qi ◽  
...  
Keyword(s):  
Common Knowledge ◽  
Traffic Classification ◽  
Knowledge Based

DISTILLER: Encrypted traffic classification via multimodal multitask deep learning

2021 ◽  
pp. 102985
Author(s):  
Giuseppe Aceto ◽  
Domenico Ciuonzo ◽  
Antonio Montieri ◽  
Antonio Pescapé
Keyword(s):  
Deep Learning ◽  
Traffic Classification ◽  
Encrypted Traffic

scholarly journals Traffic Type Recognition Method for Unknown Protocol—Applying Fuzzy Inference

Electronics ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 36
Author(s):  
Sang-Won Kim ◽  
Kee-Cheon Kim
Keyword(s):  
Fuzzy Inference ◽  
Static Field ◽  
Theoretical Background ◽  
Alignment Algorithm ◽  
Traffic Classification ◽  
Recognition Method ◽  
Inference System ◽  
Traffic Characteristics ◽  
Sequence Alignment Algorithm ◽  
Low Performance

In this paper, we propose a system that can recognize traffic types without prior knowledge of static features such as protocol header information by combining protocol analysis based on an ecological sequence alignment algorithm in a bioinformatics and fuzzy inference system. The algorithm proposed in this paper obtained up to a 91% level of performance at a similar level to several existing algorithms in experiments using datasets containing various types of traffic. In addition, it showed an excellent accuracy of 82.5% or more even under severe conditions that lowered the amount of data to a level of at least 40% or only included data in the middle of the traffic. This shows that the problem of dependence on initial data that frequently occurs in existing machine learning and deep learning-based traffic classification algorithms does not appear in the proposed algorithm. Furthermore, based on the ability to directly extract traffic characteristics without being dependent on static field values, it has secured the ability to respond with a small number of data by taking advantage of the flexibility of the membership function of the fuzzy inference engine. Through this, the applicability to low-power and low-performance environments such as IoT networks was confirmed. In this paper, we describe in detail the theoretical background for constructing such an algorithm and relevant experiments and considerations for actual verification.

Sign in / Sign up

Export Citation Format

Share Document