Abnormal traffic detection in networks of the Internet of things based on fuzzy logical inference

Author

Ageev, Sergey ; Kopchak, Yan ; Kotenko, Igor ; Saenko, Igor

Author_Institution

Mil. Signal Acad., St. Petersburg, Russia

fYear

2015

fDate

19-21 May 2015

Firstpage

5

Lastpage

8

Abstract

The paper proposes a traffic anomaly detection technique which could be implemented in networks of the Internet of things. It is based on using fuzzy logical inference applied to the stationary Poisson or self-similar traffic peculiar to networks of the Internet of things. The algorithms of the modified stochastic approximation and "sliding window", included in the traffic anomaly detection technique, are suggested. Results of an experimental assessment of the technique are discussed.

Keywords

Internet; Internet of Things; fuzzy logic; fuzzy reasoning; stochastic processes; Internet of Things; abnormal traffic detection; fuzzy logical inference; self-similar traffic; sliding window; stationary Poisson; stochastic approximation; traffic anomaly detection technique; Approximation algorithms; Approximation methods; Heuristic algorithms; Inference algorithms; Security; Stochastic processes; Telecommunication traffic; Internet of things; anomaly detection; fuzzy logical inference; stochastic approximation;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing and Measurements (SCM), 2015 XVIII International Conference on

Conference_Location

St. Petersburg

Print_ISBN

978-1-4673-6960-2

Type

conf

DOI

10.1109/SCM.2015.7190394

Filename

7190394