As network traffic grows and attacks become more prevalent and complex, we must find creative new ways to enhance intrusion detection systems (IDSes). Recently, researchers have begun to harness both machine learning and cloud computing technology to better identify threats and speed up computation times. This paper explores current research at the intersection of these two fields by examining cloud-based network intrusion detection approaches that utilize machine learning algorithms (MLAs). Specifically, we consider clustering and classification MLAs, their applicability to modern intrusion detection, and feature selection algorithms, in order to underline prominent implementations from recent research. We offer a current overview of this growing body of research, highlighting successes, challenges, and future directions for MLA-usage in cloud-based network intrusion detection approaches.

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In recent, numerous useful visual analytics tools have been designed to help domain experts solve analytical problems. However, most of the tools do not reflect the nature of solving real-world analytical tasks collaboratively because they have been designed for single users in desktop environments. In this paper, a complete visual analytics system is designed for solving real-world tasks having two integrated components: a single-user desktop system and an extended system suitable for a collaborative environment. Specifically, we designed a collaborative touch-table application (iPCA-CE) by adopting an existing single-user desktop analytical tool (iPCA). With the system, users can actively transit from individual desktop to shared collaborative environments without losing track of their analysis. They can also switch their analytical processes from collaborative to single-user workflows. To understand the usefulness of the system for solving analytical problems, we conducted a user study in both desktop and collaborative environments. From this study, we found that both applications are useful for solving analytical problems individually and collaboratively in different environments.

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