One of the critical issues in developing intrusion detection systems (IDS) in cloud-computing environments is the lack of publicly available cloud intrusion detection datasets, which hinders research into IDS in this area. There are, however, many non-cloud intrusion detection datasets. This paper seeks to leverage one of the well-established non-cloud datasets and analyze it in relation to one of the few available cloud datasets to develop a detection model using a machine learning technique. A complication is that these datasets often have different structures, contain different features and contain different, though overlapping, types of attack. The aim of this paper is to explore whether a simple machine learning classifier containing a small common feature set trained using a non-cloud dataset that has a packet-based structure can be usefully applied to detect specific attacks in the cloud dataset, which contains time-based traffic. Through this, the differences and similarities between attacks in the cloud and non-cloud datasets are analyzed and suggestions for future work are presented.
CITATION STYLE
Ahmadi, R., Macredie, R. D., & Tucker, A. (2018). Intrusion Detection Using Transfer Learning in Machine Learning Classifiers Between Non-cloud and Cloud Datasets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 11314 LNCS, pp. 556–566). Springer Verlag. https://doi.org/10.1007/978-3-030-03493-1_58
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