research-article

A Close Look at a Daily Dataset of Malware Samples

Authors:

Xabier Ugarte-Pedrero,

Mariano Graziano,

Davide BalzarottiAuthors Info & Claims

ACM Transactions on Privacy and Security (TOPS), Volume 22, Issue 1

Article No.: 6, Pages 1 - 30

https://doi.org/10.1145/3291061

Published: 22 January 2019 Publication History

Abstract

The number of unique malware samples is growing out of control. Over the years, security companies have designed and deployed complex infrastructures to collect and analyze this overwhelming number of samples. As a result, a security company can collect more than 1M unique files per day only from its different feeds. These are automatically stored and processed to extract actionable information derived from static and dynamic analysis. However, only a tiny amount of this data is interesting for security researchers and attracts the interest of a human expert.

To the best of our knowledge, nobody has systematically dissected these datasets to precisely understand what they really contain. The security community generally discards the problem because of the alleged prevalence of uninteresting samples.

In this article, we guide the reader through a step-by-step analysis of the hundreds of thousands Windows executables collected in one day from these feeds. Our goal is to show how a company can employ existing state-of-the-art techniques to automatically process these samples and then perform manual experiments to understand and document what is the real content of this gigantic dataset. We present the filtering steps, and we discuss in detail how samples can be grouped together according to their behavior to support manual verification. Finally, we use the results of this measurement experiment to provide a rough estimate of both the human and computer resources that are required to get to the bottom of the catch of the day.

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Cited By

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https://dl.acm.org/doi/10.1109/TIFS.2024.3433372
Shakir Hameed Shah SJamil Nur Rehman Khan AMohd Sidek LAlturki NMuhammad Zain Z(2024)MalRed: An innovative approach for detecting malware using the red channel analysis of color imagesEgyptian Informatics Journal10.1016/j.eij.2024.10047826(100478)Online publication date: Jun-2024
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Cheng BLeal EZhang HMing JCalandrino JTroncoso C(2023)On the feasibility of malware unpacking via hardware-assisted loop profilingProceedings of the 32nd USENIX Conference on Security Symposium10.5555/3620237.3620656(7481-7498)Online publication date: 9-Aug-2023
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Index Terms

A Close Look at a Daily Dataset of Malware Samples
1. Security and privacy

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cover image ACM Transactions on Privacy and Security

ACM Transactions on Privacy and Security Volume 22, Issue 1

February 2019

226 pages

ISSN:2471-2566

EISSN:2471-2574

DOI:10.1145/3287762

Editor:
David Basin
ETH Zurich, Switzerland

Issue’s Table of Contents

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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Publication History

Published: 22 January 2019

Accepted: 01 October 2018

Revised: 01 October 2018

Received: 01 January 2018

Published in TOPS Volume 22, Issue 1

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https://doi.org/10.1016/j.eij.2024.100478
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https://dl.acm.org/doi/10.5555/3620237.3620656
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