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Traditional data stream classification techniques assume that the stream of data is generated from a single non-stationary process. On the contrary, a recently introduced problem setting, referred to as Multistream Classification involves two independent non-stationary data generating processes. One of them is the source stream that continuously generates labeled data instances. The other one is the...
This paper describes a data driven approach to studying the science of cyber security (SoS). It argues that science is driven by data. It then describes issues and approaches towards the following three aspects: (i) Data Driven Science for Attack Detection and Mitigation, (ii) Foundations for Data Trustworthiness and Policy-based Sharing, and (iii) A Risk-based Approach to Security Metrics. We believe...
Security concerns surrounding the rise of Big Data systems have stimulated myriad new Big Data security models and implementations over the past few years. A significant disadvantage shared by most of these implementations is that they customize the underlying system source code to enforce new policies, making the customizations difficult to maintain as these layers evolve over time (e.g., over version...
Toward the ultimate goal of enhancing human performance in cyber security, we attempt to understand the cognitive components of cyber security expertise. Our initial focus is on cyber security attackers - often called “hackers”. Our first aim is to develop behavioral measures of accuracy and response time to examine the cognitive processes of pattern-recognition, reasoning and decision-making that...
We introduce an interactive computer model of two-country trade that allows students to investigate the consequences of changing economic parameters. The model is self-contained and makes no assumption concerning the existence of social welfare functions or social indifference curves. The factors of production earn incomes that lead to the demand for two goods. Students can see who are the winners...
Insider threat detection requires the identification of rare anomalies in contexts where evolving behaviors tend to mask such anomalies. This paper proposes and tests an ensemble-based stream mining algorithm based on supervised learning that addresses this challenge by maintaining an evolving collection of multiple models to classify dynamic data streams of unbounded length. The result is a classifier...
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