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

TL;DR

This paper introduces a novel one-class SVM method that incorporates privileged information during training, enhancing anomaly detection in applications like malware classification.

Contribution

The paper proposes a new one-class SVM formulation that leverages privileged information during training, improving anomaly detection performance.

Findings

Enhanced malware detection accuracy

Effective use of privileged information during training

Validated on synthetic and real datasets

Abstract

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.