Expedite feature extraction for enhanced cloud anomaly detection



Cloud computing is the latest trend in business for providing software, platforms and services over the Internet. However, a widespread adoption of this paradigm has been hampered by the lack of security mechanisms. In view of this, the aim of this work is to propose a new approach for detecting anomalies in cloud network traffic. The anomaly detection mechanism works on the basis of a Support Vector Machine (SVM). The key requirement for improving the accuracy of the SVM model, in the context of cloud, is to reduce the total amount of data. In light of this, we put forward the Poisson Moving Average predictor which is the core of the feature extraction approach and is able to handle the vast amount of information generated over time. In addition, two case studies are employed to validate the effectiveness of the mechanism on the basis of real datasets. Compared with other approaches, our solution exhibits the best performance in terms of detection and false alarm rates.


Cloud computing;Computational modeling;Feature extraction;Monitoring;Proposals;Security;Support vector machines;Security;anomaly detection;cloud computing;feature extraction;support vector machine


Anomaly detection for Cloud Computing


NOMS 2016 - 2016 IEEE/IFIP Network Operations and Management Symposium, April 2016

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Year 2017 : 1 citations

 A. S. Saljoughi, M. Mehvarz, H. Mirvaziri, Attacks and Intrusion Detection in Cloud Computing Using Neural Networks and Particle Swarm Optimization Algorithms, Emerging Science Journal, Vol. 1, No. 4, December, 2017, DOI: