Detecting Abnormal Behavior of an IoT Device in the Network Based on a Traffic Model

Telecom IT ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 50-55
Author(s):  
D. Saharov ◽  
D. Kozlov
Keyword(s):  
Machine Learning ◽  
Internet Of Things ◽  
Mobile Networks ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Abnormal Behavior ◽  
Traffic Model ◽  
The Internet ◽  
Wide Spread ◽  
The Internet Of Things

The article deals with the СoAP Protocol that regulates the transmission and reception of information traf-fic by terminal devices in IoT networks. The article describes a model for detecting abnormal traffic in 5G/IoT networks using machine learning algorithms, as well as the main methods for solving this prob-lem. The relevance of the article is due to the wide spread of the Internet of things and the upcoming update of mobile networks to the 5g generation.

Limitation of Erlang B Traffic Model in Elastic Optical Network for Blocking Probability Estimation

2018 ◽  
Vol 0 (0) ◽  
Author(s):  
Ujjwal ◽  
Jaisingh Thangaraj
Keyword(s):  
Blocking Probability ◽  
Optical Network ◽  
Constant Load ◽  
Traffic Model ◽  
Service Rate ◽  
Service Provisioning ◽  
Connection Request ◽  
Elastic Optical Network ◽  
Simulation Results ◽  
Single Path

Abstract In this paper, an algorithm for multipath connection provisioning in elastic optical network (EON) has been proposed. Initially, the algorithm prefers the single-path routing for service provisioning. But when single-path routing is not adequate to serve a dynamic connection, the algorithm switches to the connection request fragmentation. Its computation is based on the parameters such as capacity_constant and capacity_allowed to fragment the connection request on disjoint paths. Simulation results clearly state that the proposed algorithm performs well in service provisioning as compared to the traditional single-path routing algorithms and improves the average network throughput. Thereafter, we have investigated the limitation of Erlang B traffic model in EON for calculation of link blocking probability using routing and spectrum assignment (RSA) algorithm. It is verified by the following two ways: (i) effect on the blocking probability in case of constant load and (ii) effect of slot width on the blocking probability. Our simulation results indicate that in EON due to dynamic RSA, blocking probability is not constant in case of proportionate varying of call arrival and service rate giving constant load and blocking probability depends on the number of slots per link, but in Erlang B traffic model blocking probability is always constant in case of constant load and it considers wavelength per link instead of slots per link. This is attributed to the fact that Erlang B traffic model fails to calculate blocking probability accurately in EON. We have computed the carried traffic on 14 nodes, 21-link National Science Foundation Network (NSFNET) topology.

An extended continuum mixed traffic model

2021 ◽  
Author(s):  
Yicai Zhang ◽  
Min Zhao ◽  
Dihua Sun ◽  
Chen Dong
Keyword(s):  
Traffic Model ◽  
Mixed Traffic
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