StealthML: Data-driven Malware for Stealthy Data Exfiltration

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of machine learning methods have been actively studied to detect and mitigate the consequences of malicious attacks. However, this sophisticated technology can become a threat when it falls into the wrong hands. This paper describes a new class of malware that employs machine learning to autonomously infer when and how to trigger an attack payload to maximize impact while minimizing attack traces. We designed, implemented, and demonstrated a smart malware that monitors the realtime network traffic flow of the victim system, analyzes the collected traffic data to forecast traffic and identify the most opportune time to trigger data extraction, and optimizes its strategy in planning the data exfiltration to minimize traces that might reveal the malware's presence.

Original languageEnglish (US)
Title of host publicationProceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages16-21
Number of pages6
ISBN (Electronic)9798350311709
DOIs
StatePublished - 2023
Event3rd IEEE International Conference on Cyber Security and Resilience, CSR 2023 - Hybrid, Venice, Italy
Duration: Jul 31 2023Aug 2 2023

Publication series

NameProceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023

Conference

Conference3rd IEEE International Conference on Cyber Security and Resilience, CSR 2023
Country/TerritoryItaly
CityHybrid, Venice
Period7/31/238/2/23

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Information Systems
  • Safety, Risk, Reliability and Quality

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