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Support Vector Machines (SVM) is a widely used technique for classifying high-dimensional data, especially in security and intelligence categorization. However, the performance of SVM can be adversely affected by poorly selected parameter values. Current approaches to SVM parameter selection mainly rely on extensive cross validation or anecdotal information, which can be inefficient and ineffective...
As cybercrimes and their data volumes proliferate, business professionals and public servants urgently need new knowledge and skills to address the growing threats. However, curricular materials, pedagogical research, and courses to address the data deluge in cybersecurity are not widely available. This research developed a contextual active learning approach to creating curricular modules for use...
Among the most important and distinctive actionable knowledge are actionable behavioral rules (ABRs). To make ABRM a promising technique for security informatics, we develop new methodologies for it. We also conduct an experiment to validate our approach. The experimental results strongly suggest the validity of our approach.
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