@inproceedings{hu-etal-2022-controllable,
title = "Controllable Fake Document Infilling for Cyber Deception",
author = "Hu, Yibo and
Lin, Yu and
Skorupa Parolin, Erick and
Khan, Latifur and
Hamlen, Kevin",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2022",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-emnlp.486",
doi = "10.18653/v1/2022.findings-emnlp.486",
pages = "6505--6519",
abstract = "Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information. However, existing approaches are context-agnostic, resulting in sub-optimal and unvaried outputs. We propose a novel context-aware model, Fake Document Infilling (FDI), by converting the problem to a controllable mask-then-infill procedure. FDI masks important concepts of varied lengths in the document, then infills a realistic but fake alternative considering both the previous and future contexts. We conduct comprehensive evaluations on technical documents and news stories. Results show that FDI outperforms the baselines in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.",
}
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<abstract>Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information. However, existing approaches are context-agnostic, resulting in sub-optimal and unvaried outputs. We propose a novel context-aware model, Fake Document Infilling (FDI), by converting the problem to a controllable mask-then-infill procedure. FDI masks important concepts of varied lengths in the document, then infills a realistic but fake alternative considering both the previous and future contexts. We conduct comprehensive evaluations on technical documents and news stories. Results show that FDI outperforms the baselines in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.</abstract>
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%0 Conference Proceedings
%T Controllable Fake Document Infilling for Cyber Deception
%A Hu, Yibo
%A Lin, Yu
%A Skorupa Parolin, Erick
%A Khan, Latifur
%A Hamlen, Kevin
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Findings of the Association for Computational Linguistics: EMNLP 2022
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F hu-etal-2022-controllable
%X Recent works in cyber deception study how to deter malicious intrusion by generating multiple fake versions of a critical document to impose costs on adversaries who need to identify the correct information. However, existing approaches are context-agnostic, resulting in sub-optimal and unvaried outputs. We propose a novel context-aware model, Fake Document Infilling (FDI), by converting the problem to a controllable mask-then-infill procedure. FDI masks important concepts of varied lengths in the document, then infills a realistic but fake alternative considering both the previous and future contexts. We conduct comprehensive evaluations on technical documents and news stories. Results show that FDI outperforms the baselines in generating highly believable fakes with moderate modification to protect critical information and deceive adversaries.
%R 10.18653/v1/2022.findings-emnlp.486
%U https://aclanthology.org/2022.findings-emnlp.486
%U https://doi.org/10.18653/v1/2022.findings-emnlp.486
%P 6505-6519
Markdown (Informal)
[Controllable Fake Document Infilling for Cyber Deception](https://aclanthology.org/2022.findings-emnlp.486) (Hu et al., Findings 2022)
ACL
- Yibo Hu, Yu Lin, Erick Skorupa Parolin, Latifur Khan, and Kevin Hamlen. 2022. Controllable Fake Document Infilling for Cyber Deception. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 6505–6519, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.