Systemically Evaluating the Robustness of ML-based IoT Malware Detectors

Ahmed Abusnaina, Afsah Anwar, Sultan Alshamrani, Abdulrahman Alabduljabbar, Rhongho Jang, Daehun Nyang, David Mohaisen

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

The rapid growth of the Internet of Things (IoT) devices is paralleled by them being on the front-line of malicious attacks caused by malicious software. Machine learning (ML) algorithms, alongside the traditional signature-based methods, are typically used to detect malicious activities and behaviors. However, they are susceptible to malware evolution and sophistication, making them limited to the patterns that they have been trained upon. In this work, we systematically examine the state-of-The-Art malware detection approaches using various representations, under a range of adversarial settings. Our preliminary analyses highlight the instability of the learning algorithms in learning patterns that distinguish the benign from the malicious. Our mutations with functionality-preserving operations, e.g., software stripping and binary padding, significantly deteriorate the accuracy of malware detectors.

Original languageEnglish
Title of host publicationProceedings - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3-4
Number of pages2
ISBN (Electronic)9781665435666
DOIs
StatePublished - Jun 2021
Event51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2021 - Virtual, Taipei, Taiwan, Province of China
Duration: 21 Jun 202124 Jun 2021

Publication series

NameProceedings - 51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2021

Conference

Conference51st Annual IEEE/IFIP International Conference on Dependable Systems and Networks - Supplemental Volume, DSN-S 2021
Country/TerritoryTaiwan, Province of China
CityVirtual, Taipei
Period21/06/2124/06/21

Bibliographical note

Funding Information:
IV. CONCLUSION Despite advancesin AI-backed system defenses, the systems have been shown to be vulnerable. With this work, we systematically evaluated the state of a range of malware detectors, proposed by the research community and industry-standard. Our effort unveils the status-quo of the existing detectors, and brings forward various insights to consider when proposing detection systems, particularly, the ML model robustness. Acknowledgement. This work was supported in part by NRF under grant 2016K1A1A2912757 and a CyberFlorida Collaborative Seed Award.

Publisher Copyright:
© 2021 IEEE.

Keywords

  • Adversarial Machine Learning
  • Internet of Things
  • Malware Detection

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