A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security
Anomaly detection in many applications is becoming more and more important, especially for security and privacy in mobile service computing domains with the development of mobile internet and mobile cloud computing, in which data are typical multidimensional time series data. However, the collective...
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/8691875/ |
| _version_ | 1818854536992784384 |
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| author | Yu Weng Lei Liu |
| author_facet | Yu Weng Lei Liu |
| author_sort | Yu Weng |
| collection | DOAJ |
| description | Anomaly detection in many applications is becoming more and more important, especially for security and privacy in mobile service computing domains with the development of mobile internet and mobile cloud computing, in which data are typical multidimensional time series data. However, the collective anomaly detection for multidimensional streams exists lots of problems, owing to the differences between the anomaly detection in multidimensional time series and univariate time series data. For example, the temporal continuity of multidimensional time series is much weaker than univariate time series and it is unreasonable to judge the entire multidimensional data as an anomaly if a certain dimension is abnormal. In this paper, we consider the statistical features of the subsequence of streams, proposing a novel collective anomaly detection algorithm for multidimensional streams based on iForest in a cloud environment, namely iForestFS. When using different features about mobile cloud services' metrics suggested by domain knowledge, iForestFS could detect different kinds of anomalies for mobile service security. Furthermore, we implement a distributed iForestFS using spark framework in order to improve time performance and scalability. The experimental results performed on three datasets (mainly about network security) derived from the UCI repository demonstrate that the proposed algorithm can effectively detect a collective anomaly of multidimensional streams in the security domain. |
| first_indexed | 2024-12-19T07:54:17Z |
| format | Article |
| id | doaj.art-e65c401dfd4f42b7bd373e4288e15900 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-19T07:54:17Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e65c401dfd4f42b7bd373e4288e159002022-12-21T20:30:03ZengIEEEIEEE Access2169-35362019-01-017491574916810.1109/ACCESS.2019.29097508691875A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service SecurityYu Weng0https://orcid.org/0000-0002-0787-550XLei Liu1School of Information Engineering, Minzu University of China, Beijing, ChinaSchool of Information Engineering, Minzu University of China, Beijing, ChinaAnomaly detection in many applications is becoming more and more important, especially for security and privacy in mobile service computing domains with the development of mobile internet and mobile cloud computing, in which data are typical multidimensional time series data. However, the collective anomaly detection for multidimensional streams exists lots of problems, owing to the differences between the anomaly detection in multidimensional time series and univariate time series data. For example, the temporal continuity of multidimensional time series is much weaker than univariate time series and it is unreasonable to judge the entire multidimensional data as an anomaly if a certain dimension is abnormal. In this paper, we consider the statistical features of the subsequence of streams, proposing a novel collective anomaly detection algorithm for multidimensional streams based on iForest in a cloud environment, namely iForestFS. When using different features about mobile cloud services' metrics suggested by domain knowledge, iForestFS could detect different kinds of anomalies for mobile service security. Furthermore, we implement a distributed iForestFS using spark framework in order to improve time performance and scalability. The experimental results performed on three datasets (mainly about network security) derived from the UCI repository demonstrate that the proposed algorithm can effectively detect a collective anomaly of multidimensional streams in the security domain.https://ieeexplore.ieee.org/document/8691875/Collective anomaly detectiontime seriesisolation forestsecurity in mobile servicemultidimensional stream processing |
| spellingShingle | Yu Weng Lei Liu A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security IEEE Access Collective anomaly detection time series isolation forest security in mobile service multidimensional stream processing |
| title | A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security |
| title_full | A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security |
| title_fullStr | A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security |
| title_full_unstemmed | A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security |
| title_short | A Collective Anomaly Detection Approach for Multidimensional Streams in Mobile Service Security |
| title_sort | collective anomaly detection approach for multidimensional streams in mobile service security |
| topic | Collective anomaly detection time series isolation forest security in mobile service multidimensional stream processing |
| url | https://ieeexplore.ieee.org/document/8691875/ |
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