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In this paper we present a method of functionally classifying malicious code that might lead to automated attacks and intrusions using kernel machines. We study the performance of kernel methods in the context of robustness and generalization capabilities of malware classification. Current static detection and scanning techniques for malicious code and malware have serious limitations; on the other...
Tao et. al. have recently proposed the posterior probability support vector machine (PPSVM) which uses soft labels derived from estimated posterior probabilities to be more robust to noise and outliers. Tao et. al.'s model uses a window-based density estimator to calculate the posterior probabilities and is a binary classifier. We propose a neighbor-based density estimator and also extend the model...
The dynamic time warping (DTW) is state-of-the-art distance measure widely used in sequential pattern matching and it outperforms Euclidean distance in most cases because its matching is elastic and robust. It is tempting to substitute DTW distance for Euclidean distance in the Gaussian RBF kernel and plug it into the state-of-the art classifier support vector machines (SVMs) for sequence classification...
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