# Finding Cryptocurrency Attack Indicators Using Temporal Logic and Darkweb Data

arXiv: https://arxiv.org/abs/1810.12906 · HTML: https://mlsanity.com/paper/1810.12906/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Oct 2018.

## Built on by (most cited first, 1 of 1)

- [Darknet Traffic Big-Data Analysis and Network Management to Real-Time Automating the Malicious Intent Detection Process by a Weight Agnostic Neural Networks Framework](https://mlsanity.com/paper/2102.08411/) (2021)

## Abstract (arXiv)

With the recent prevalence of darkweb/deepweb (D2web) sites specializing in the trade of exploit kits and malware, malicious actors have easy-access to a wide-range of tools that can empower their offensive capability. In this study, we apply concepts from causal reasoning, itemset mining, and logic programming on historical cryptocurrency-related cyber incidents with intelligence collected from over 400 D2web hacker forums. Our goal was to find indicators of cyber threats targeting cryptocurrency traders and exchange platforms from hacker activity. Our approach found interesting activities that, when observed together in the D2web, subsequent cryptocurrency-related incidents are at least twice as likely to occur than they would if no activity was observed. We also present an algorithmic extension to a previously-introduced algorithm called APT-Extract that allows to model new semantic structures that are specific to our application.