Incremental Learning for Malware Classification in Small Datasets

Abstract

Information security is an important research area. As a very special yet important case, malware classification plays an important role in information security. In the real world, the malware datasets are open-ended and dynamic, and new malware samples belonging to old classes and new classes are increasing continuously. This requires the malware classification method to enable incremental learning, which can efficiently learn the new knowledge. However, existing works mainly focus on feature engineering with machine learning as a tool. To solve the problem, we present an incremental malware classification framework, named “IMC,” which consists of opcode sequence extraction, selection, and incremental learning method. We develop an incremental learning method based on multiclass support vector machine (SVM) as the core component of IMC, named “IMCSVM,” which can incrementally improve its classification ability by learning new malware samples. In IMC, IMCSVM adds the new classification planes (if new samples belong to a new class) and updates all old classification planes for new malware samples. As a result, IMC can improve the classification quality of known malware classes by minimizing the prediction error and transfer the old model with known knowledge to classify unknown malware classes. We apply the incremental learning method into malware classification, and the experimental results demonstrate the advantages and effectiveness of IMC.