Contextual Chart Generation for Cyber Deception
David D. Nguyen David Liebowitz Surya Nepal Salil S. Kanhere Sharif Abuadbba
Honeyfiles are security assets designed to attract and detect intruders on
compromised systems. Honeyfiles are a type of honeypot that mimic real,
sensitive documents, creating the illusion of the presence of valuable data.
Interaction with a honeyfile reveals the presence of an intruder, and can
provide insights into their goals and intentions. Their practical use, however,
is limited by the time, cost and effort associated with manually creating
realistic content. The introduction of large language models has made
high-quality text generation accessible, but honeyfiles contain a variety of
content including charts, tables and images. This content needs to be plausible
and realistic, as well as semantically consistent both within honeyfiles and
with the real documents they mimic, to successfully deceive an intruder.
  In this paper, we focus on an important component of the honeyfile content
generation problem: document charts. Charts are ubiquitous in corporate
documents and are commonly used to communicate quantitative and scientific
data. Existing image generation models, such as DALL-E, are rather prone to
generating charts with incomprehensible text and unconvincing data. We take a
multi-modal approach to this problem by combining two purpose-built generative
models: a multitask Transformer and a specialized multi-head autoencoder. The
Transformer generates realistic captions and plot text, while the autoencoder
generates the underlying tabular data for the plot.
  To advance the field of automated honeyplot generation, we also release a new
document-chart dataset and propose a novel metric Keyword Semantic Matching
(KSM). This metric measures the semantic consistency between keywords of a
corpus and a smaller bag of words. Extensive experiments demonstrate excellent
performance against multiple large language models, including ChatGPT and GPT4.