k NN Classification of Malware in HTTPS Traffic Using the Metric Space Approach
In this paper, we present detection of malware in HTTPS traffic using k NN classification. We focus on the metric space approach for approximate k NN searches over dataset of sparse high dimensional descriptors of network traffic. We show the classification based on approximate k NN search using metric index exhibits false positive rate reduced by an order of magnitude when compared to the state of the art method, while keeping the classification fast enough.
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