Application Communities Detection in Network

The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose...

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Main Authors: Shuzhuang Zhang, Yingjun Qiu, Hao Luo, Zhigang Wu
Format: Article
Language:English
Published: MDPI AG 2018-12-01
Series:Applied Sciences
Subjects:
Online Access:http://www.mdpi.com/2076-3417/9/1/31
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spelling doaj-74f0475d670f4f2184e56651cf8fb5872020-11-24T22:23:02ZengMDPI AGApplied Sciences2076-34172018-12-01913110.3390/app9010031app9010031Application Communities Detection in NetworkShuzhuang Zhang0Yingjun Qiu1Hao Luo2Zhigang Wu3Institute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInstitute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInstitute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaInstitute of Network Technology, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaThe continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security.http://www.mdpi.com/2076-3417/9/1/31application communitycommunication behavior modetopological relationshipcluster
collection DOAJ
language English
format Article
sources DOAJ
author Shuzhuang Zhang
Yingjun Qiu
Hao Luo
Zhigang Wu
spellingShingle Shuzhuang Zhang
Yingjun Qiu
Hao Luo
Zhigang Wu
Application Communities Detection in Network
Applied Sciences
application community
communication behavior mode
topological relationship
cluster
author_facet Shuzhuang Zhang
Yingjun Qiu
Hao Luo
Zhigang Wu
author_sort Shuzhuang Zhang
title Application Communities Detection in Network
title_short Application Communities Detection in Network
title_full Application Communities Detection in Network
title_fullStr Application Communities Detection in Network
title_full_unstemmed Application Communities Detection in Network
title_sort application communities detection in network
publisher MDPI AG
series Applied Sciences
issn 2076-3417
publishDate 2018-12-01
description The continuous growth of Internet traffic and its applications causes more difficulties for analyzing Internet communications. It has become an increasingly challenging task to discover latent community structure and find abnormal behavior patterns in network communication. In this paper, we propose a new type of network community—the application community—which can help understand large network structure and find anomaly network behavior. To detect such a community, a method is proposed whose first step is aggregating the nodes according to their topological relationships of the communication. It then clusters different application nodes according to the communication behavior modes in the same topological partition. Empirical results show that this method can accurately detect communities of different applications without any prior knowledge. In addition, it can identify the communities more accurately than other methods. Thus, this research greatly benefits the administration of IoT and cyber security.
topic application community
communication behavior mode
topological relationship
cluster
url http://www.mdpi.com/2076-3417/9/1/31
work_keys_str_mv AT shuzhuangzhang applicationcommunitiesdetectioninnetwork
AT yingjunqiu applicationcommunitiesdetectioninnetwork
AT haoluo applicationcommunitiesdetectioninnetwork
AT zhigangwu applicationcommunitiesdetectioninnetwork
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