Anomaly Detection System in Cloud Environment Using Fuzzy Clustering Based ANN
Autor: | N. Pandeeswari, Ganesh Kumar |
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Rok vydání: | 2015 |
Předmět: |
Fuzzy clustering
Computer Networks and Communications Computer science Anomaly-based intrusion detection system business.industry 020206 networking & telecommunications Cloud computing 02 engineering and technology Intrusion detection system Machine learning computer.software_genre Constant false alarm rate Naive Bayes classifier ComputingMethodologies_PATTERNRECOGNITION Hardware and Architecture 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Anomaly detection Data mining Artificial intelligence Cluster analysis business computer Software Information Systems |
Zdroj: | Mobile Networks and Applications. 21:494-505 |
ISSN: | 1572-8153 1383-469X |
Popis: | Cloud computing affords lot of resources and computing facilities through Internet. Cloud systems attract many users with its desirable features. In spite of them, Cloud systems may experience severe security issues. Thus, it is essential to create an Intrusion Detection System (IDS) to detect both insider and outsider attacks with high detection accuracy in cloud environment. This work proposes an anomaly detection system at the hypervisor layer named Hypervisor Detector that uses a hybrid algorithm which is a mixture of Fuzzy C-Means clustering algorithm and Artificial Neural Network (FCM-ANN) to improve the accuracy of the detection system. The proposed system is implemented and compared with Naive Bayes classifier and Classic ANN algorithm. The DARPA's KDD cup dataset 1999 is used for experiments. Based on extensive theoretical and performance analysis, it is evident that the proposed system is able to detect the anomalies with high detection accuracy and low false alarm rate even for low frequent attacks thereby outperforming Naive Bayes classifier and Classic ANN. |
Databáze: | OpenAIRE |
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