TITLE:
Generation of Labelled Datasets to Quantify the Impact of Security Threats to Cloud Data Centers
AUTHORS:
Sai Kiran Mukkavilli, Sachin Shetty, Liang Hong
KEYWORDS:
Amazon Web Services, Anomaly Detection, Cloud Computing, Datasets, Intrusion Detection, Network Traces
JOURNAL NAME:
Journal of Information Security,
Vol.7 No.3,
April
14,
2016
ABSTRACT: 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 Also, publicly available datasets are either outdated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.