1,315 research outputs found
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
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
On the Activity Privacy of Blockchain for IoT
Security is one of the fundamental challenges in the Internet of Things (IoT)
due to the heterogeneity and resource constraints of the IoT devices. Device
classification methods are employed to enhance the security of IoT by detecting
unregistered devices or traffic patterns. In recent years, blockchain has
received tremendous attention as a distributed trustless platform to enhance
the security of IoT. Conventional device identification methods are not
directly applicable in blockchain-based IoT as network layer packets are not
stored in the blockchain. Moreover, the transactions are broadcast and thus
have no destination IP address and contain a public key as the user identity,
and are stored permanently in blockchain which can be read by any entity in the
network. We show that device identification in blockchain introduces privacy
risks as the malicious nodes can identify users' activity pattern by analyzing
the temporal pattern of their transactions in the blockchain. We study the
likelihood of classifying IoT devices by analyzing their information stored in
the blockchain, which to the best of our knowledge, is the first work of its
kind. We use a smart home as a representative IoT scenario. First, a blockchain
is populated according to a real-world smart home traffic dataset. We then
apply machine learning algorithms on the data stored in the blockchain to
analyze the success rate of device classification, modeling both an informed
and a blind attacker. Our results demonstrate success rates over 90\% in
classifying devices. We propose three timestamp obfuscation methods, namely
combining multiple packets into a single transaction, merging ledgers of
multiple devices, and randomly delaying transactions, to reduce the success
rate in classifying devices. The proposed timestamp obfuscation methods can
reduce the classification success rates to as low as 20%
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