One-Class SVM with Privileged Information and its Application to Malware Detection

2016-09-26 · Evgeny Burnaev, Dmitry Smolyakov

A number of important applied problems in engineering, finance and medicine can be formulated as a problem of anomaly detection. A classical approach to the problem is to describe a normal state using a one-class support vector machine. Then to detect anomalies we quantify a distance from a new observation to the constructed description of the normal class. In this paper we present a new approach to the one-class classification. We formulate a new problem statement and a corresponding algorithm that allow taking into account a privileged information during the training phase. We evaluate performance of the proposed approach using a synthetic dataset, as well as the publicly available Microsoft Malware Classification Challenge dataset.

📄 PDF Abstract BibTeX arXiv:1609.08039

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Tasks

Anomaly Detection Classification General Classification Malware Classification Malware DetectionOne-Class Classification

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