Jozsef Hegedus

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—In this paper, a two-stage methodology to analyze and detect behavioral-based malware is presented. In the first stage, a random projection is decreasing the variable dimensionality of the problem and is simultaneously reducing the computational time of the classification task by several orders of magnitude. In the second stage, a modified K-Nearest(More)
This paper focuses on the problem of making decisions in the context of nominal data under specific constraints. The underlying goal driving the methodology proposed here is to build a decision-making model capable of classifying as many samples as possible while avoiding false positives at all costs, all within the smallest possible computational time.(More)
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