Controllable Fake Document Infilling for Cyber Deception (2022)

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.

## introduction according to the statement of the u.s. securities and exchange commission, the scope and severity of cyber risks have dramatically increased, and constant vigilance is needed to protect against intrusion @xcite . cyber deception is a cybersecurity defense practice @xcite @xcite ) that aims at protecting critical documents once intruders penetrate the network system @xcite . the goal is to deceive attackers by deploying decoys such as fake documents and thus increase their cost to identify critical information. in this work, we aim at designing a novel fake document generator that combines cyber deception and natural language generation (nlg) technologies to generate controllable, diverse, and believable fakes at scale to protect critical information and deceive adversaries. although recent works in cyber deception develop strategies to generate complicated fake technical documents such as patents, few consider adopting pretrained contextual features to enhance scalability and generation quality. for example, forge @xcite generates fake documents by replacing the concepts of a technical document with semantically similar alternatives from an expensive prerequisite ontology. we-forge @xcite eliminates the need for ontologies by using word embedding distances. however, it identifies potential replacements only for unigrams (especially nouns) based on unbalanced word embedding clusters in a context-agnostic manner, resulting in suboptimal or inadequate alternatives. meanwhile, recent studies in nlg have been driven by pre-trained contextual language models (lms), which can generate increasingly realistic but less-controllable text @xcite @xcite . sub-fields such as controllable text generation @xcite , story generation @xcite , and text infilling @xcite further study how to leverage lms to generate content with desired attributes. however, few methods offer fine-grained control over concept levels or provide an efficient, controllable fake text generation strategy. we propose a novel context-aware model, fake document infilling (fdi), by converting fake document generation into a controllable mask-then-infill procedure. specifically, we select and mask essential concepts of varied lengths in a document. then we infill the masked spans with realistic but fake alternatives based on contextualized knowledge from an lm. to the best of our knowledge, we are the first to propose a complete controllable mask-theninfill model and design a comprehensive evaluation scheme to study fake text generation. to briefly demonstrate the motivation for this work, both we-forge and fdi adopt the strategy of replacing specific concepts (shown in blue in c and e) in the original document with alternatives (shown in red in d and f). however, we-forge suffers from three major limitations. first, we-forge needs to train word-embeddings from scratch for every custom dataset, requiring large training corpora @xcite . second, its word-embedding-based clustering of concepts is unbalanced and sensitive to the initialization and hyper-parameters, resulting in limited replacements for some given concepts. finally, we-forge only replaces nouns and is agnostic to context, limiting the diversity and quality of the generated text. in contrast, fdi provides many advantages over previous methods. first, instead of training word embeddings from scratch, fdi finetunes a pretrained lm to generate human-like text with limited data. furthermore, fdi replaces spans of ar-bitrary lengths, considering the document context (through the lm) to improve the outputs' diversity and coherency. finally, fdi implements strategies to select (mask) and find alternative concepts (infill), protecting essential details from original documents and producing realistic fake samples. to validate the outperformance of fdi, we design an innovative set of experiments combining evaluation methods observed in distinct areas, i.e., cyber security and nlg. we collect reviews from more than 40 volunteers over 1.4k fake documents on technical and non-technical datasets. finally, we compile the reviews to evaluate the model's ability to generate natural text and its effectiveness in protecting the original information and deterring attackers. our code is publicly available. 1 2 related work cyber deception. cyber deception aims at deceiving attackers by misguiding them toward inaccurate information with deployed decoys in the network systems of enterprises. early works generate decoy honey files @xcite @xcite or simple documents with basic nlp methods @xcite to entice attackers and improve intrusion and exfiltration detection. recent works combine advanced nlp techniques to generate fake technical documents at scale while enhancing believability. these efforts include substituting words or concepts based on part-of-speech tagging @xcite , prerequisite ontologies @xcite , concept occurrences graphs @xcite , or word embeddings @xcite . nevertheless, these methods are context-agnostic, limiting producing diverse and natural outputs. the only exception @xcite uses vanilla contextualized lms on short description texts instead of long technical documents. controllable text generation. building costly conditional lms for desired attributes, by either training from scratch @xcite or back-propagating gradients @xcite , are extensively studied. the attributes are usually pre-defined by a list of control codes or keywords. other lightweight alternatives are proposed by using discriminators or bayes' rules to control the attributes of generated text during the decoding time @xcite @xcite . a subfield called story generation focuses on generating short stories given hints such as title, storyline, premise, entities, or rare words @xcite @xcite @xcite @xcite @xcite . specifically, @xcite generate fake news stories conditioned on metadata from a list of propaganda websites. nevertheless, these fields differ from our task. they mainly focus on non-technical domains (e.g., news and stories) and lack fine-grained control over concept levels. text infilling. text infilling is a generalization of the cloze task @xcite from single words to spans of varied lengths. current works focus on correctly infilling the incomplete text for applications in text editing or ancient documents restoration @xcite @xcite @xcite . however, the controllable mask-then-infill task addressed in this paper is more complex. it involves masking relevant concepts (text spans) in a document and infilling realistic yet misleading spans to replace such masks. adversarial augmentation. this task aims at generating perturbed augmented samples to improve the robustness of nlp models by heuristic rules that replace words from wordnet or word embeddings @xcite @xcite , contextualized perturbations @xcite @xcite , or comprehensive frameworks @xcite @xcite . again, these random perturbation methods lack precise control over concepts, hindering their usage for our task. ## conclusion and future work we propose a novel fake document generator, fdi, for network intrusion defense and intellectual property protection. fdi relies on a complete maskthen-infill process with a curated strategy for fake documents generation. our experiments explore "how easily the original documents are identified" and "how critical information is protected" with more fake samples and generation patterns. fdi shows consistent superiority in generating realistic fake samples while protecting the information and deceiving the hackers. while human evaluation remains the gold standard for evaluating various nlg applications, future work can explore automatic detection methods @xcite @xcite to alleviate human efforts. besides, this work focuses on technical documents and shows generalization in news stories. future work can also extend its applications to other critical domains, such as political science @xcite @xcite @xcite .