Author(s)

Sai Kiran Mukkavilli, Sachin Shetty, Liang Hong

Department of Electrical & Computer Engineering, Tennessee State University, Nashville, TN, USA.

Anomaly based approaches in network intrusion detection suffer from evaluation, comparison and deployment which originate from the scarcity of adequate publicly available network trace datasets. Also, publicly available datasets are either outdated or generated in a controlled environment. Due to the ubiquity of cloud computing environments in commercial and government internet services, there is a need to assess the impacts of network attacks in cloud data centers. To the best of our knowledge, there is no publicly available dataset which captures the normal and anomalous network traces in the interactions between cloud users and cloud data centers. In this paper, we present an experimental platform designed to represent a practical interaction between cloud users and cloud services and collect network traces resulting from this interaction to conduct anomaly detection. We use Amazon web services (AWS) platform for conducting our experiments.

Amazon Web Services, Anomaly Detection, Cloud Computing, Datasets, Intrusion Detection, Network Traces

Mukkavilli, S. , Shetty, S. and Hong, L. (2016) Generation of Labelled Datasets to Quantify the Impact of Security Threats to Cloud Data Centers. Journal of Information Security, 7, 172-184. doi: 10.4236/jis.2016.73013.