220 research outputs found
AI-powered Fraud Detection in Decentralized Finance: A Project Life Cycle Perspective
In recent years, blockchain technology has introduced decentralized finance
(DeFi) as an alternative to traditional financial systems. DeFi aims to create
a transparent and efficient financial ecosystem using smart contracts and
emerging decentralized applications. However, the growing popularity of DeFi
has made it a target for fraudulent activities, resulting in losses of billions
of dollars due to various types of frauds. To address these issues, researchers
have explored the potential of artificial intelligence (AI) approaches to
detect such fraudulent activities. Yet, there is a lack of a systematic survey
to organize and summarize those existing works and to identify the future
research opportunities. In this survey, we provide a systematic taxonomy of
various frauds in the DeFi ecosystem, categorized by the different stages of a
DeFi project's life cycle: project development, introduction, growth, maturity,
and decline. This taxonomy is based on our finding: many frauds have strong
correlations in the stage of the DeFi project. According to the taxonomy, we
review existing AI-powered detection methods, including statistical modeling,
natural language processing and other machine learning techniques, etc. We find
that fraud detection in different stages employs distinct types of methods and
observe the commendable performance of tree-based and graph-related models in
tackling fraud detection tasks. By analyzing the challenges and trends, we
present the findings to provide proactive suggestion and guide future research
in DeFi fraud detection. We believe that this survey is able to support
researchers, practitioners, and regulators in establishing a secure and
trustworthy DeFi ecosystem.Comment: 38 pages, update reference
Detecting potential money laundering addresses in the Bitcoin blockchain using unsupervised machine learning
Money laundering is a serious problem worldwide, especially in the crypto market. This is mostly because of the anonymity that many cryptocurrencies offer. That is one of the reasons why cryptocurrencies are a haven for money laundering, because it is easier for criminal entities to buy the currency and then trade it for real fiat money. Detecting money laundering in cryptocurrency can be tricky because the crypto network is large and convoluted and nearly impossible to analyze by hand. What we can do is look at addresses that took part in transactions as actors and then use machine learning to predict what addresses are possibly laundering money. In this paper we intend to analyze methods that can be used to detect money laundering in Bitcoin using machine learning to empower investigators to more accurately and efficiently determine whether a suspicious activity is money laundering
Data mining for detecting Bitcoin Ponzi schemes
Soon after its introduction in 2009, Bitcoin has been adopted by
cyber-criminals, which rely on its pseudonymity to implement virtually
untraceable scams. One of the typical scams that operate on Bitcoin are the
so-called Ponzi schemes. These are fraudulent investments which repay users
with the funds invested by new users that join the scheme, and implode when it
is no longer possible to find new investments. Despite being illegal in many
countries, Ponzi schemes are now proliferating on Bitcoin, and they keep
alluring new victims, who are plundered of millions of dollars. We apply data
mining techniques to detect Bitcoin addresses related to Ponzi schemes. Our
starting point is a dataset of features of real-world Ponzi schemes, that we
construct by analysing, on the Bitcoin blockchain, the transactions used to
perform the scams. We use this dataset to experiment with various machine
learning algorithms, and we assess their effectiveness through standard
validation protocols and performance metrics. The best of the classifiers we
have experimented can identify most of the Ponzi schemes in the dataset, with a
low number of false positives
Anti-Money Laundering Alert Optimization Using Machine Learning with Graphs
Money laundering is a global problem that concerns legitimizing proceeds from
serious felonies (1.7-4 trillion euros annually) such as drug dealing, human
trafficking, or corruption. The anti-money laundering systems deployed by
financial institutions typically comprise rules aligned with regulatory
frameworks. Human investigators review the alerts and report suspicious cases.
Such systems suffer from high false-positive rates, undermining their
effectiveness and resulting in high operational costs. We propose a machine
learning triage model, which complements the rule-based system and learns to
predict the risk of an alert accurately. Our model uses both entity-centric
engineered features and attributes characterizing inter-entity relations in the
form of graph-based features. We leverage time windows to construct the dynamic
graph, optimizing for time and space efficiency. We validate our model on a
real-world banking dataset and show how the triage model can reduce the number
of false positives by 80% while detecting over 90% of true positives. In this
way, our model can significantly improve anti-money laundering operations.Comment: 8 pages, 5 figure
Pattern Analysis of Money Flow in the Bitcoin Blockchain
Bitcoin is the first and highest valued cryptocurrency that stores
transactions in a publicly distributed ledger called the blockchain.
Understanding the activity and behavior of Bitcoin actors is a crucial research
topic as they are pseudonymous in the transaction network. In this article, we
propose a method based on taint analysis to extract taint flows --dynamic
networks representing the sequence of Bitcoins transferred from an initial
source to other actors until dissolution. Then, we apply graph embedding
methods to characterize taint flows. We evaluate our embedding method with
taint flows from top mining pools and show that it can classify mining pools
with high accuracy. We also found that taint flows from the same period show
high similarity. Our work proves that tracing the money flows can be a
promising approach to classifying source actors and characterizing different
money flow pattern
Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity
Lorenz, J., Silva, M. I., Aparício, D., Ascensão, J. T., & Bizarro, P. (2020). Machine learning methods to detect money laundering in the bitcoin blockchain in the presence of label scarcity. In ICAIF 2020 - 1st ACM International Conference on AI in Finance (pp. 1-8). [3422549] (ICAIF 2020 - 1st ACM International Conference on AI in Finance). Association for Computing Machinery, Inc. https://doi.org/10.1145/3383455.3422549Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking), harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Then, we show that our proposed active learning solution is capable of matching the performance of a fully supervised baseline by using just 5% of the labels. This solution mimics a typical real-life situation in which a limited number of labels can be acquired through manual annotation by experts.publishersversionpublishe
Cascading Machine Learning to Attack Bitcoin Anonymity
Bitcoin is a decentralized, pseudonymous cryptocurrency that is one of the
most used digital assets to date. Its unregulated nature and inherent anonymity
of users have led to a dramatic increase in its use for illicit activities.
This calls for the development of novel methods capable of characterizing
different entities in the Bitcoin network. In this paper, a method to attack
Bitcoin anonymity is presented, leveraging a novel cascading machine learning
approach that requires only a few features directly extracted from Bitcoin
blockchain data. Cascading, used to enrich entities information with data from
previous classifications, led to considerably improved multi-class
classification performance with excellent values of Precision close to 1.0 for
each considered class. Final models were implemented and compared using
different machine learning models and showed significantly higher accuracy
compared to their baseline implementation. Our approach can contribute to the
development of effective tools for Bitcoin entity characterization, which may
assist in uncovering illegal activities.Comment: 15 pages,7 figures, 4 tables, presented in 2019 IEEE International
Conference on Blockchain (Blockchain
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