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Mobile Anomaly Detection Based on Improved Self-Organizing Maps
Joint Authors
Yin, Chunyong
Zhang, Sun
Kim, Kwang-jun
Source
Issue
Vol. 2017, Issue 2017 (31 Dec. 2017), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2017-02-12
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Telecommunications Engineering
Abstract EN
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.
American Psychological Association (APA)
Yin, Chunyong& Zhang, Sun& Kim, Kwang-jun. 2017. Mobile Anomaly Detection Based on Improved Self-Organizing Maps. Mobile Information Systems،Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1189104
Modern Language Association (MLA)
Yin, Chunyong…[et al.]. Mobile Anomaly Detection Based on Improved Self-Organizing Maps. Mobile Information Systems No. 2017 (2017), pp.1-9.
https://search.emarefa.net/detail/BIM-1189104
American Medical Association (AMA)
Yin, Chunyong& Zhang, Sun& Kim, Kwang-jun. Mobile Anomaly Detection Based on Improved Self-Organizing Maps. Mobile Information Systems. 2017. Vol. 2017, no. 2017, pp.1-9.
https://search.emarefa.net/detail/BIM-1189104
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1189104