How to Cover up Anomalous Accesses to Electronic Health Records


Xiaojun Xu, Qingying Hao, Zhuolin Yang, and Bo Li, University of Illinois at Urbana-Champaign; David Liebovitz, Northwestern University; Gang Wang and Carl A. Gunter, University of Illinois at Urbana-Champaign


Illegitimate access detection systems in hospital logs perform post hoc detection instead of runtime access restriction to allow widespread access in emergencies. We study the effectiveness of adversarial machine learning strategies against such detection systems on a large-scale dataset consisting of a year of access logs at a major hospital. We study a range of graph-based anomaly detection systems, including heuristic-based and Graph Neural Network (GNN)-based models. We find that evasion attacks, in which covering accesses (that is, accesses made to disguise a target access) are injected during evaluation period of the target access, can successfully fool the detection system. We also show that such evasion attacks can transfer among different detection algorithms. On the other hand, we find that poisoning attacks, in which adversaries inject covering accesses during the training phase of the model, do not effectively mislead the trained detection system unless the attacker is given unrealistic capabilities such as injecting over 10,000 accesses or imposing a high weight on the covering accesses in the training algorithm. To examine the generalizability of the results, we also apply our attack against a state-of-the-art detection model on the LANL network lateral movement dataset, and observe similar conclusions.

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@inproceedings {287302,
author = {Xiaojun Xu and Qingying Hao and Zhuolin Yang and Bo Li and David Liebovitz and Gang Wang and Carl A. Gunter},
title = {How to Cover up Anomalous Accesses to Electronic Health Records},
booktitle = {32nd USENIX Security Symposium (USENIX Security 23)},
year = {2023},
isbn = {978-1-939133-37-3},
address = {Anaheim, CA},
pages = {229--246},
url = {},
publisher = {USENIX Association},
month = aug

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