TSM: Measuring the Enticement of Honeyfiles with Natural Language Processing

TL;DR

This paper introduces TSM, a novel NLP-based metric for measuring honeyfile enticement by comparing topical content and semantic similarity, aiding cyber deception strategies.

Contribution

The paper presents TSM, the first NLP-based metric for quantifying honeyfile enticement through topic modeling and semantic matching, validated with a new honeyfile corpus.

Findings

TSM effectively compares honeyfiles across different corpora.

TSM is robust to paraphrasing and captures topical similarity.

Experiments demonstrate TSM's potential in cyber deception.

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.