446 research outputs found
The Evolution of Embedding Metadata in Blockchain Transactions
The use of blockchains is growing every day, and their utility has greatly
expanded from sending and receiving crypto-coins to smart-contracts and
decentralized autonomous organizations. Modern blockchains underpin a variety
of applications: from designing a global identity to improving satellite
connectivity. In our research we look at the ability of blockchains to store
metadata in an increasing volume of transactions and with evolving focus of
utilization. We further show that basic approaches to improving blockchain
privacy also rely on embedding metadata. This paper identifies and classifies
real-life blockchain transactions embedding metadata of a number of major
protocols running essentially over the bitcoin blockchain. The empirical
analysis here presents the evolution of metadata utilization in the recent
years, and the discussion suggests steps towards preventing criminal use.
Metadata are relevant to any blockchain, and our analysis considers primarily
bitcoin as a case study. The paper concludes that simultaneously with both
expanding legitimate utilization of embedded metadata and expanding blockchain
functionality, the applied research on improving anonymity and security must
also attempt to protect against blockchain abuse.Comment: 9 pages, 6 figures, 1 table, 2018 International Joint Conference on
Neural Network
The Rise of Crypto Malware: Leveraging Machine Learning Techniques to Understand the Evolution, Impact, and Detection of Cryptocurrency-Related Threats
Crypto malware has become a major threat to the security of cryptocurrency holders and exchanges. As the popularity of cryptocurrency continues to rise, so too does the number and sophistication of crypto malware attacks. This paper leverages machine learning techniques to understand the evolution, impact, and detection of cryptocurrency-related threats. We analyse the different types of crypto malware, including ransomware, crypto jacking, and supply chain attacks, and explore the use of machine learning algorithms for detecting and preventing these threats. Our research highlights the importance of using machine learning for detecting crypto malware and compares the effectiveness of traditional methods with deep learning techniques. Through this analysis, we aim to provide insights into the growing threat of crypto malware and the potential benefits of using machine learning in combating these attacks
Adversarial behaviours knowledge area
The technological advancements witnessed by our society in recent decades have brought
improvements in our quality of life, but they have also created a number of opportunities for
attackers to cause harm. Before the Internet revolution, most crime and malicious activity
generally required a victim and a perpetrator to come into physical contact, and this limited
the reach that malicious parties had. Technology has removed the need for physical contact
to perform many types of crime, and now attackers can reach victims anywhere in the world, as long as they are connected to the Internet. This has revolutionised the characteristics of crime and warfare, allowing operations that would not have been possible before. In this document, we provide an overview of the malicious operations that are happening on the Internet today. We first provide a taxonomy of malicious activities based on the attacker’s motivations and capabilities, and then move on to the technological and human elements that adversaries require to run a successful operation. We then discuss a number of frameworks that have been proposed to model malicious operations. Since adversarial behaviours are not a purely technical topic, we draw from research in a number of fields (computer science, criminology, war studies). While doing this, we discuss how these frameworks can be used by researchers and practitioners to develop effective mitigations against malicious online operations.Published versio
Cryptocurrency scams: analysis and perspectives
Since the inception of Bitcoin in 2009, the market of cryptocurrencies has grown beyond the initial expectations, as witnessed by the thousands of tokenised assets available on the market, whose daily trades amount to dozens of USD billions. The pseudonymity features of these cryptocurrencies have attracted the attention of cybercriminals, who exploit them to carry out potentially untraceable scams. The wide range of cryptocurrency-based scams observed over the last ten years has fostered the research on the analysis of their effects, and the development of techniques to counter them. However, doing research in this field requires addressing several challenges: for instance, although a few data sources about cryptocurrency scams are publicly available, they often contain incomplete or misclassified data. Further, there is no standard taxonomy of scams, which leads to ambiguous and incoherent interpretations of their nature. Indeed, the unavailability of reliable datasets makes it difficult to train effective automatic classifiers that can detect and analyse cryptocurrency scams. In this paper, we perform an extensive review of the scientific literature on cryptocurrency scams, which we systematise according to a novel taxonomy. By collecting and homogenising data from different public sources, we build a uniform dataset of thousands of cryptocurrency scams.We devise an automatic tool that recognises scams and classifies them according to our taxonomy.We assess the effectiveness of our tool through standard performance metrics.We also give an in-depth analysis of the classification results, offering several insights into threat types, from their features to their connection with other types. Finally, we provide a set of guidelines that policymakers could follow to improve user protection against cryptocurrency scams
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
The Evolution of Embedding Metadata in Blockchain Transactions
The use of blockchains is growing every day, and
their utility has greatly expanded from sending and receiving
crypto-coins to smart-contracts and decentralized autonomous
organizations. Modern blockchains underpin a variety of applications: from designing a global identity to improving satellite
connectivity. In our research we look at the ability of blockchains
to store metadata in an increasing volume of transactions and
with evolving focus of utilization. We further show that basic approaches to improving blockchain privacy also rely on embedding
metadata. This paper identifies and classifies real-life blockchain
transactions embedding metadata of a number of major protocols
running essentially over the bitcoin blockchain. The empirical
analysis here presents the evolution of metadata utilization in the
recent years, and the discussion suggests steps towards preventing
criminal use. Metadata are relevant to any blockchain, and our
analysis considers primarily bitcoin as a case study. The paper
concludes that simultaneously with both expanding legitimate
utilization of embedded metadata and expanding blockchain
functionality, the applied research on improving anonymity and
security must also attempt to protect against blockchain abuse
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