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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Bibliographic Details
Published inMobile information systems Vol. 2017; no. 2017; pp. 1 - 9
Main Authors Yin, Chunyong, Kim, Kwang-jun, Zhang, Sun
Format Journal Article
LanguageEnglish
Published Cairo, Egypt Hindawi Publishing Corporation 01.01.2017
Hindawi
Hindawi Limited
Subjects
Anomalies
Clustering
Data mining
Datasets
Electronic devices
Internet
Night
Self organizing maps
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Summary: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.
ISSN:1574-017X
1875-905X
DOI:10.1155/2017/5674086