Mobile Anomaly Detection Based on Improved Self-Organizing Maps
Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack no...
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Online Access: | http://dx.doi.org/10.1155/2017/5674086 |
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doaj-4a7ddf40599c4fba99580f12b6fe44d92021-07-02T02:39:46ZengHindawi LimitedMobile Information Systems1574-017X1875-905X2017-01-01201710.1155/2017/56740865674086Mobile Anomaly Detection Based on Improved Self-Organizing MapsChunyong Yin0Sun Zhang1Kwang-jun Kim2School of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaSchool of Computer and Software, Jiangsu Engineering Center of Network Monitoring, Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology, Jiangsu Key Laboratory of Meteorological Observation and Information Processing, Nanjing University of Information Science & Technology, Nanjing 210044, ChinaDepartment of Computer Engineering, Chonnam National University, Gwangju, Republic of KoreaAnomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate.http://dx.doi.org/10.1155/2017/5674086 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Chunyong Yin Sun Zhang Kwang-jun Kim |
spellingShingle |
Chunyong Yin Sun Zhang Kwang-jun Kim Mobile Anomaly Detection Based on Improved Self-Organizing Maps Mobile Information Systems |
author_facet |
Chunyong Yin Sun Zhang Kwang-jun Kim |
author_sort |
Chunyong Yin |
title |
Mobile Anomaly Detection Based on Improved Self-Organizing Maps |
title_short |
Mobile Anomaly Detection Based on Improved Self-Organizing Maps |
title_full |
Mobile Anomaly Detection Based on Improved Self-Organizing Maps |
title_fullStr |
Mobile Anomaly Detection Based on Improved Self-Organizing Maps |
title_full_unstemmed |
Mobile Anomaly Detection Based on Improved Self-Organizing Maps |
title_sort |
mobile anomaly detection based on improved self-organizing maps |
publisher |
Hindawi Limited |
series |
Mobile Information Systems |
issn |
1574-017X 1875-905X |
publishDate |
2017-01-01 |
description |
Anomaly detection has always been the focus of researchers and especially, the developments of mobile devices raise new challenges of anomaly detection. For example, mobile devices can keep connection with Internet and they are rarely turned off even at night. This means mobile devices can attack nodes or be attacked at night without being perceived by users and they have different characteristics from Internet behaviors. The introduction of data mining has made leaps forward in this field. Self-organizing maps, one of famous clustering algorithms, are affected by initial weight vectors and the clustering result is unstable. The optimal method of selecting initial clustering centers is transplanted from K-means to SOM. To evaluate the performance of improved SOM, we utilize diverse datasets and KDD Cup99 dataset to compare it with traditional one. The experimental results show that improved SOM can get higher accuracy rate for universal datasets. As for KDD Cup99 dataset, it achieves higher recall rate and precision rate. |
url |
http://dx.doi.org/10.1155/2017/5674086 |
work_keys_str_mv |
AT chunyongyin mobileanomalydetectionbasedonimprovedselforganizingmaps AT sunzhang mobileanomalydetectionbasedonimprovedselforganizingmaps AT kwangjunkim mobileanomalydetectionbasedonimprovedselforganizingmaps |
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1721342968422465536 |