Unveiling Zeus

TL;DR

This paper presents a behavior-based machine learning approach to classify Zeus malware using 65 robust features, achieving up to 95% accuracy, demonstrating the effectiveness of artifact analysis in malware detection.

Contribution

It introduces a novel dataset and a set of 65 features for behavior-based malware classification, achieving high accuracy in identifying Zeus malware.

Findings

High classification accuracy up to 95%

Behavioral artifacts effectively distinguish malware families

New dataset enhances malware research

Abstract

Malware family classification is an age old problem that many Anti-Virus (AV) companies have tackled. There are two common techniques used for classification, signature based and behavior based. Signature based classification uses a common sequence of bytes that appears in the binary code to identify and detect a family of malware. Behavior based classification uses artifacts created by malware during execution for identification. In this paper we report on a unique dataset we obtained from our operations and classified using several machine learning techniques using the behavior-based approach. Our main class of malware we are interested in classifying is the popular Zeus malware. For its classification we identify 65 features that are unique and robust for identifying malware families. We show that artifacts like file system, registry, and network features can be used to identify distinct malware families with high accuracy---in some cases as high as 95%.