883 research outputs found
Traffic congestion in interconnected complex networks
Traffic congestion in isolated complex networks has been investigated
extensively over the last decade. Coupled network models have recently been
developed to facilitate further understanding of real complex systems. Analysis
of traffic congestion in coupled complex networks, however, is still relatively
unexplored. In this paper, we try to explore the effect of interconnections on
traffic congestion in interconnected BA scale-free networks. We find that
assortative coupling can alleviate traffic congestion more readily than
disassortative and random coupling when the node processing capacity is
allocated based on node usage probability. Furthermore, the optimal coupling
probability can be found for assortative coupling. However, three types of
coupling preferences achieve similar traffic performance if all nodes share the
same processing capacity. We analyze interconnected Internet AS-level graphs of
South Korea and Japan and obtain similar results. Some practical suggestions
are presented to optimize such real-world interconnected networks accordingly.Comment: 8 page
Streaming phishing scam detection method on Ethereum
Phishing is a widespread scam activity on Ethereum, causing huge financial
losses to victims. Most existing phishing scam detection methods abstract
accounts on Ethereum as nodes and transactions as edges, then use manual
statistics of static node features to obtain node embedding and finally
identify phishing scams through classification models. However, these methods
can not dynamically learn new Ethereum transactions. Since the phishing scams
finished in a short time, a method that can detect phishing scams in real-time
is needed. In this paper, we propose a streaming phishing scam detection
method. To achieve streaming detection and capture the dynamic changes of
Ethereum transactions, we first abstract transactions into edge features
instead of node features, and then design a broadcast mechanism and a storage
module, which integrate historical transaction information and neighbor
transaction information to strengthen the node embedding. Finally, the node
embedding can be learned from the storage module and the previous node
embedding. Experimental results show that our method achieves decent
performance on the Ethereum phishing scam detection task
Does Money Laundering on Ethereum Have Traditional Traits?
As the largest blockchain platform that supports smart contracts, Ethereum
has developed with an incredible speed. Yet due to the anonymity of blockchain,
the popularity of Ethereum has fostered the emergence of various illegal
activities and money laundering by converting ill-gotten funds to cash. In the
traditional money laundering scenario, researchers have uncovered the prevalent
traits of money laundering. However, since money laundering on Ethereum is an
emerging means, little is known about money laundering on Ethereum. To fill the
gap, in this paper, we conduct an in-depth study on Ethereum money laundering
networks through the lens of a representative security event on \textit{Upbit
Exchange} to explore whether money laundering on Ethereum has traditional
traits. Specifically, we construct a money laundering network on Ethereum by
crawling the transaction records of \textit{Upbit Hack}. Then, we present five
questions based on the traditional traits of money laundering networks. By
leveraging network analysis, we characterize the money laundering network on
Ethereum and answer these questions. In the end, we summarize the findings of
money laundering networks on Ethereum, which lay the groundwork for money
laundering detection on Ethereum
Financial Crimes in Web3-empowered Metaverse: Taxonomy, Countermeasures, and Opportunities
At present, the concept of metaverse has sparked widespread attention from
the public to major industries. With the rapid development of blockchain and
Web3 technologies, the decentralized metaverse ecology has attracted a large
influx of users and capital.
Due to the lack of industry standards and regulatory rules, the
Web3-empowered metaverse ecosystem has witnessed a variety of financial crimes,
such as scams, code exploit, wash trading, money laundering, and illegal
services and shops. To this end, it is especially urgent and critical to
summarize and classify the financial security threats on the Web3-empowered
metaverse in order to maintain the long-term healthy development of its
ecology.
In this paper, we first outline the background, foundation, and applications
of the Web3 metaverse. Then, we provide a comprehensive overview and taxonomy
of the security risks and financial crimes that have emerged since the
development of the decentralized metaverse. For each financial crime, we focus
on three issues: a) existing definitions, b) relevant cases and analysis, and
c) existing academic research on this type of crime. Next, from the perspective
of academic research and government policy, we summarize the current anti-crime
measurements and technologies in the metaverse. Finally, we discuss the
opportunities and challenges in behavioral mining and the potential regulation
of financial activities in the metaverse.
The overview of this paper is expected to help readers better understand the
potential security threats in this emerging ecology, and to provide insights
and references for financial crime fighting.Comment: 24pages, 6 figures, 140 references, submitted to the Open Journal of
the Computer Societ
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