TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing

Abstract

Honeyfile deployment is a useful breach detection method in cyber deception that can also inform defenders about the intent and interests of intruders and malicious insiders. A key property of a honeyfile, enticement, is the extent to which the file can attract an intruder to interact with it. We introduce a novel metric, Topic Semantic Matching (TSM), which uses topic modelling to represent files in the repository and semantic matching in an embedding vector space to compare honeyfile text and topic words robustly. We also present a honeyfile corpus created with different Natural Language Processing (NLP) methods. Experiments show that TSM is effective in inter-corpus comparisons and is a promising tool to measure the enticement of honeyfiles. TSM is the first measure to use NLP techniques to quantify the enticement of honeyfile content that compares the essential topical content of local contexts to honeyfiles and is robust to paraphrasing.

Deception for Cyber Defence: Challenges and Opportunities

“…Earlier work on enticement [24] measured common word counts as a basis for a metric. Since this approach does not account for paraphrasing, we have introduced a measure called the Topic Semantic Matching (TSM) enticement score [41]. TSM extracts the main topics of a repository, also know as local context, using topic modelling.…”

“…Development of the SCHEMADB dataset -a collection of relational schema in both MySQL and heterogeneous graph form[60].• Development of a honeyfile corpus for use in experiments on measuring the enticement of honeyfiles[41].…”