TY - GEN
T1 - StealthML
T2 - 3rd IEEE International Conference on Cyber Security and Resilience, CSR 2023
AU - Chung, Keywhan
AU - Cao, Phuong
AU - Kalbarczyk, Zbigniew T.
AU - Iyer, Ravishankar K.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85171745222&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85171745222&partnerID=8YFLogxK
U2 - 10.1109/CSR57506.2023.10224946
DO - 10.1109/CSR57506.2023.10224946
M3 - Conference contribution
AN - SCOPUS:85171745222
T3 - Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023
SP - 16
EP - 21
BT - Proceedings of the 2023 IEEE International Conference on Cyber Security and Resilience, CSR 2023
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 31 July 2023 through 2 August 2023
ER -