Expedite Feature Extraction for Enhanced Cloud Anomaly Detection

• Type: Article in International Conference Proceedings Book
• Date: 2016

Authors

Dalmazo, BL (Author) Other; João P. Vilela (Author) Other; Simoes, P (Author) Other; Curado, M (Author) Other

Conference proceedings International

Title: NOMS 2016 - 2016 IEEE/IFIP NETWORK OPERATIONS AND MANAGEMENT SYMPOSIUM

Pages: 1215-1220

IEEE/IFIP Network Operations and Management Symposium (NOMS)

Istanbul, TURKEY, APR 25-29, 2016

Indexing

ISI Web of Knowledge - 13 Citations

Scopus - 19 Citations

Other information

Authenticus ID: P-00K-PZ6

DOI: 10.1109/noms.2016.7502990

Abstract (EN): 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 6