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Improving Trustworthiness in DeFi: Anomaly Detection for Blockchain Oracle Price Feed Data
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Resumo(s)
This thesis investigates the application of anomaly detection techniques to blockchain oracle
data, specifically focusing on the Ethereum - US Dollar exchange rate from the Chainlink
oracle. The study addresses a critical gap in the reliability of decentralized finance applications
by exploring both traditional static machine learning algorithms and novel streaming methods
for identifying irregularities in oracle data feeds. The research compares the performance of
various anomaly detection algorithms, including Support Vector Machines (SVM), DBSCAN, KMeans, Self-Organizing Maps (SOM), K-Nearest Neighbors (KNN), and Isolation Forest for
static methods, and Sliding Window KNN (SWKNN), Robust Random Cut Forest (RRCF), and
Half-Space Trees for streaming methods. These algorithms are evaluated using F1 score and
recall as primary metrics, with a focus on their ability to detect sudden price spikes.
Furthermore, the study examines the incorporating financial indicators such as Relative
Strength Index, Average True Range, and Exponential Moving Average to enhance anomaly
detection capabilities. The research utilizes UMAP projections for initial visual analysis and
conducts comprehensive comparisons between the originally fetched univariate dataset and
different multivariate datasets enhanced with additional features in form of financial
indicators. The results indicate that multivariate datasets generally improve F1 scores, for
static as well as streaming methods, while the streaming methods achieve a higher baseline
on univariate datasets, compared to the static methods.
This research contributes to the field by addressing the understudied aspect of anomaly
detection in blockchain oracle data, providing insights into the effectiveness of various
machine learning techniques in enhancing the security and reliability of DeFi applications.
Descrição
Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Blockchain Smart Contracts Decentralized Finance Blockchain Oracles Anomaly Detection Streaming Data