Malware Triage for Early Identification of Advanced Persistent Threat Activities

Abstract

In the past decade, a new class of cyber-threats, known as “Advanced Persistent Threat” (APT), has emerged and has been used by different organizations to perform dangerous and effective attacks against financial and politic entities, critical infrastructures, and so on. To identify APT related malware early, a semi-automatic approach for malware samples analysis is needed. Recently, a malware triage step for a semi-automatic malware analysis architecture has been introduced. This step identifies incoming APT samples early, among all the malware delivered per day in the cyber-space, to immediately dispatch them to deeper analysis. In the article, the authors have built the knowledge base on known APTs obtained from publicly available reports. For efficiency reasons, they rely on static malware features, extracted with negligible delay, and use machine learning techniques for the identification. Unfortunately, the proposed solution has the disadvantage of requiring a long training time and needs to be completely retrained each time new APT samples or even a new APT class are discovered. In this article, we move from multi-class classification to a group of one-class classifiers, which significantly decreases runtime and allows higher modularity, while still guaranteeing precision and accuracy over 90%.