Adam B. Turner, Stephen McCombie and Allon J. Uhlmann
This paper aims to demonstrate the utility of a target-centric approach to intelligence collection and analysis in the prevention and investigation of ransomware attacks that…
Abstract
Purpose
This paper aims to demonstrate the utility of a target-centric approach to intelligence collection and analysis in the prevention and investigation of ransomware attacks that involve cryptocurrencies. The paper uses the May 2017 WannaCry ransomware usage of the Bitcoin ecosystem as a case study. The approach proves particularly beneficial in facilitating information sharing and an integrated analysis across intelligence domains.
Design/methodology/approach
This study conducted data collection and analysis of the component Bitcoin elements of the WannaCry ransomware attack. A note of both technicalities of Bitcoin operations and current models for sharing cyber intelligence was made. Our analysis builds on and further develops current definitions and strategies for sharing cyber threat intelligence. It uses the problem definition model (PDM) and generic target network model (TNM) to create an analytic framework for the WannaCry ransomware attack scenario, allowing analysts the ability to test their hypotheses and integrate and share data for collaborative investigation.
Findings
Using a target-centric intelligence approach to WannaCry 2.0 shows that it is possible to model the intelligence problem of collecting and analysing data related to inflows and outflows of Bitcoin-related ransomware transactions. Bitcoin transactions form graph networks and allow to build a target network model for collecting, analysing and sharing intelligence with multiple stakeholders. Although attribution and anonymity prevail under cryptocurrency usage, there is a means for developing transaction walks using this method to target nefarious cryptocurrency exchanges where criminals are inclined to cash out their proceeds of crime.
Originality/value
The application of a target-centric intelligence approach to the cryptocurrency components of a ransomware attack provides a framework for intelligence units to break down the problem in the financial domain and model the network behaviour of illicit Bitcoin transactions relating to ransomware.
Details
Keywords
Adam B. Turner, Stephen McCombie and Allon J. Uhlmann
The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the…
Abstract
Purpose
The purpose of this paper is to investigate available forensic data on the Bitcoin blockchain to identify typical transaction patterns of ransomware attacks. Specifically, the authors explore how distinct these patterns are and their potential value for intelligence exploitation in support of countering ransomware attacks.
Design/methodology/approach
The authors created an analytic framework – the Ransomware–Bitcoin Intelligence–Forensic Continuum framework – to search for transaction patterns in the blockchain records from actual ransomware attacks. Data of a number of different ransomware Bitcoin addresses was extracted to populate the framework, via the WalletExplorer.com programming interface. This data was then assembled in a representation of the target network for pattern analysis on the input (cash-in) and output (cash-out) side of the ransomware seed addresses. Different graph algorithms were applied to these networks. The results were compared to a “control” network derived from a Bitcoin charity.
Findings
The findings show discernible patterns in the network relating to the input and output side of the ransomware graphs. However, these patterns are not easily distinguishable from those associated with the charity Bitcoin address on the input side. Nonetheless, the collection profile over time is more volatile than with the charity Bitcoin address. On the other hand, ransomware output patterns differ from those associated charity addresses, as the attacker cash-out tactics are quite different from the way charities mobilise their donations. We further argue that an application of graph machine learning provides a basis for future analysis and data refinement possibilities.
Research limitations/implications
Limitations are evident in the sample size of data taken on ransomware campaigns and the “control” subject. Further analysis of additional ransomware campaigns and “control” subjects over time would help refine and validate the preliminary observations in this paper. Future research will also benefit from the application of more powerful computing resources and analytics platforms that scale with the amount of data being collected.
Originality/value
This research contributes to the maturity of the field by analysing ransomware-Bitcoin behaviour using the Ransomware–Bitcoin Intelligence–Forensic Continuum. By combining several different techniques to discerning patterns of ransomware activity on the Bitcoin network, it provides insight into whether a ransomware attack is occurring and could be used to trigger alerts to seek additional evidence of attack, or could corroborate other information in the system.