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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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 |
_version_ |
1725766235305541632 |