One-Class SVM with Privileged Information and its Application to Malware Detection
This incremental improvement addresses anomaly detection problems in fields like engineering, finance, and medicine, specifically for malware detection.
The paper tackled anomaly detection by introducing a new one-class SVM formulation that incorporates privileged information during training, and demonstrated its effectiveness on a synthetic dataset and the Microsoft Malware Classification Challenge dataset.
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.