Kriptosare.gen, a Dockerized Bitcoin Testbed: Analysis of Server Performance

Zola, Francesco; Pérez-Solà, Cristina; Zubia, Jon Egaña; Eguimendia, María; Herrera-Joancomartí, Jordi

doi:10.1109/ntms.2019.8763809

Cited by 1 publication

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“…This would allow researchers to replicate "typical" entity behavior or specific types of attacks or illicit activities related to certain entities. Furthermore, the controlled environment, for example the one presented in [50], could be used to detect limitations of the protocol, creating specific scenarios or defining new entity behavior not yet discovered in the mainnet.…”

Section: Discussionmentioning

confidence: 99%

Bitcoin and Cybersecurity: Temporal Dissection of Blockchain Data to Unveil Changes in Entity Behavioral Patterns

et al. 2019

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The Bitcoin network not only is vulnerable to cyber-attacks but currently represents the most frequently used cryptocurrency for concealing illicit activities. Typically, Bitcoin activity is monitored by decreasing anonymity of its entities using machine learning-based techniques, which consider the whole blockchain. This entails two issues: first, it increases the complexity of the analysis requiring higher efforts and, second, it may hide network micro-dynamics important for detecting short-term changes in entity behavioral patterns. The aim of this paper is to address both issues by performing a “temporal dissection” of the Bitcoin blockchain, i.e., dividing it into smaller temporal batches to achieve entity classification. The idea is that a machine learning model trained on a certain time-interval (batch) should achieve good classification performance when tested on another batch if entity behavioral patterns are similar. We apply cascading machine learning principles—a type of ensemble learning applying stacking techniques—introducing a “k-fold cross-testing” concept across batches of varying size. Results show that blockchain batch size used for entity classification could be reduced for certain classes (Exchange, Gambling, and eWallet) as classification rates did not vary significantly with batch size; suggesting that behavioral patterns did not change significantly over time. Mixer and Market class detection, however, can be negatively affected. A deeper analysis of Mining Pool behavior showed that models trained on recent data perform better than models trained on older data, suggesting that “typical” Mining Pool behavior may be represented better by recent data. This work provides a first step towards uncovering entity behavioral changes via temporal dissection of blockchain data.

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Section: Discussionmentioning

confidence: 99%