Crypto analysis course doc
A blockchain is a permanent, sequential list of transaction records distributed over a network. Each block in the chain contains a hash of the previous block, along with a timestamp and transaction data. This makes the blockchain inherently resistant to attack or manipulation. Blockchain technology is ideal for recording various types of transactions where data is sensitive or targeted by hackers for unauthorized duplication or other fraudulent activity. Bitcoin and other cryptocurrencies use blockchain technology to record transactions. Blockchain for business applications can include recording of contracts, medical records, monetary transactions and much more.
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Crypto analysis course doc
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A Guide to Cryptocurrency Fundamental Analysis
This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal, and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points. This paper examines the current literature on the analysis of illicit Bitcoin transactions and focuses specifically on the analytic techniques that are applied to blockchain data.
These illicit Bitcoin transactions could take the form of money laundering, terrorism financing or the movement of proceeds from other crimes such as ransomware attacks.
Many of the techniques wrestle with the problem of attribution in the face of the anonymity of sources within the Bitcoin ecosystem. Therefore, we first examine the body of literature relating to regulatory efforts that aim to balance the freedom of an open system with the requirements of crime prevention and law enforcement. Following that is a review of the research into the techniques that exploit heuristics and behaviors inherent in the Bitcoin system. We then highlight the application of graph analysis techniques to the Bitcoin ecosystem and transaction networks.
Furthermore, Machine Learning ML and Artificial Intelligence AI techniques applied to money laundering, cybercrime and other illicit activities across the Bitcoin ecosystem are reviewed. The decentralized nature of the peer to peer network from which Nakamoto designed Bitcoin affords the user anonymity and bypasses the central authority used to regulate traditional financial systems.
Tsukerman surveys the state of the Bitcoin regulatory environment from a United States US centric position. To help understand this environment they provide a breakdown of the laws into two categories: those laws that protect consumers who use Bitcoin; and those that address the broader societal impacts of people using Bitcoin for illegal purposes such as money laundering and terrorist financing Tsukerman, Tu and Meredith complement the work by Tsukerman by considering the impediments to effective regulation of Bitcoin which addresses the issues of ownership, attribution and the susceptibility to theft, that virtual currencies are subject to.
Reclamation of these stolen funds is identified as a major risk to users by Tu and Meredith In addition, these systems do not collect the necessary Personal Identifiable Information PII that will allow for the implementation of strict financial transaction reporting procedures for the purposes of mitigating illicit financial activity and the misappropriation of funds Irwin and Turner, For each of these jurisdictions, there is a foreign law specialist assigned to assess the legal conditions within the respective jurisdiction.
During the introduction of this report, foreign law specialist Hanibal Goitom identifies the major issues jurisdictions are facing. By revealing how different countries are legally operating cryptocurrency markets in their jurisdictions the report highlights specific laws enacted for cryptocurrency markets to operate and the contrasting jurisdictions that restrict their trade.
It identifies the likes of Belarus, Gibraltar, Jersey, and Mexico have enacted laws specifically recognizing cryptocurrency markets. The Decree sets out a specific economic zone for companies to operate cryptocurrency related exchanges and services.
In contrast countries such as China and Iran are excluding financial institutions within their jurisdiction from engaging in cryptocurrency markets. For instance, Pilarowski and Yue identify eight entities in China providing governance and oversight on the prevention of cryptocurrency usage. The reason sighted was down to investor protection and financial risk prevention Pilarowski and Yue, Tax evasion is an important but peripheral topic to this paper, however, Goitom, from The Law Library of Congress highlights the issue of how cryptocurrencies are taxed across various jurisdictions.
This is a wide-ranging debate on the application of Tax Law against how cryptocurrencies are treated as a financial instrument. The Tax debate falls outside the scope of this review. In addition, risks need to be individually assessed for specific types of services and customers, how these services will be delivered to the customers, any foreign jurisdictions being traversed, and the state of connection of any financial entity performing a service in a foreign jurisdiction.
In addition, the 5th Anti-Money Laundering Directive of the European Union EU, provides a legislative framework for the prevention and detection of money laundering and terrorism financing in virtual currencies and exchanges. Section 9. Irwin and Turner emphasize KYC and CDD as critical for linking the real-world identity of a customer's behavior and developing an understanding of their expected financial activities.
FATF provides recommendations and standards for over jurisdictions to help prevent money laundering and terrorism financing. Clearly, law enforcement agencies lack globally consistent procedures, laws, regulations or standards to police the misuse of cryptocurrencies. The FATF strives to set out global standards to combat money laundering and terrorist financing, and other significant threats that exist to disrupt the integrity of the global financial system.
According to The Law Library of Congress a number of countries are beginning to look at regulating cryptocurrencies and formulating policy frameworks. Albeit a subjective link, CipherTrace suggest that this could be down to the AML controls inhibiting the exchange or cash-out of illicit proceeds.
This along with the EU directive , underlines the significance of enabling authorities to monitor the use of virtual currencies. Section 8. Challenges remain anchored in the international nature of cryptocurrency transactions and any resultant cybercriminal activity. To counter this challenge, it will be essential to prevent offenders from hopping from one jurisdiction to another.
The application of more stringent provisions could risk stifling the innovative functionality of cryptocurrencies, but at the same time balance out any illicit usage by having the capability to reveal the true identity of those participating in cryptocurrency.
However, for the tradeoffs to be effective international cooperation, information sharing and monitoring between law enforcement agencies, FIUs and cryptocurrency service providers will be required.
This type of monitoring demands analysis techniques based on graph theory and network analysis which can produce predictive features and a machine learning architecture to manage large datasets.
Implementation of machine learning architectures is intended to improve monitoring and investigations over time and would be less manpower intensive. In the next section we will review the literature pertaining to such techniques.
After the release of the Nakamoto whitepaper, A peer-to-peer electronic cash system. Bitcoin , the early analysis of Bitcoin revolved around understanding the mechanics of the system. This is evident in Kaminsky who presented findings on the interaction of the Bitcoin protocol with Internet security protocols. In addition, Rosenfeld examined how the mining process works in order to reward participants on the Bitcoin network, Karame et al.
Then Stokes , broke ground on the utility of virtual currencies applied to money laundering. Investigation into illicit Bitcoin usage creates a mosaic of information that must be forensically reconstructed to provide an accurate view of the target.
The information can be technological, behavioral, criminological and regulatory in nature. The introduction of heuristics into the analysis can help address the difficulties of attribution. This is achieved by grouping similar transactional behavior and linking ownership to addresses and services on the Bitcoin network.
Meiklejohn et al. The heuristics presented within this paper form the basis of which much of today's Bitcoin analysis is performed. This work makes it possible to cluster activity around a certain user and add context to this user for purposes of identification or grouping similar services on the network.
In addition, they discover, if a user of an input address also controls a one-off change address associated to that transaction, it may be assumed that both addresses are owned by the same user. This common pattern can be used to obfuscate the movement of funds and result in the detection of money laundering on the Bitcoin network.
Drilling deeper into the payment trends allows for a more targeted understanding of illicit user activity, especially its source. They also determined that it was only possible to identify ownership after any suspicious activity had occurred. Predicting that suspicious activity is going to take place in the future requires the collection of targeted Bitcoin addresses or transaction IDs to learn and train models for future prediction, investigation and analysis.
Therefore, there is a need to look at other information sources to determine possible fraudulent transactions. This is where Reid and Harrigan posited cluster analysis as a technique to reveal patterns, associations, structures and relationships emanating from different data sources.
Clustering can be used to identify common entities on the Bitcoin network controlling Bitcoin addresses by building up a picture of transaction flows over time.
Nakamoto , implies that clustering algorithms can group together multiple input transactions controlled by the same address, potentially identifying the owner of the address Nakamoto, This makes it possible to construct a user network identifying mappings between Bitcoin addresses and a cluster of similar users Reid and Harrigan, There is also the potential to find connection between Bitcoin addresses, IP addresses and spending patterns through this type of analysis.
To de-anonymize users on the Bitcoin network, Turner and Irwin look at the openness of the Bitcoin system and some of the defining features seen within the anatomy of a Bitcoin transaction coupled with extensive data collection from packet sniffing software. Using network traffic analyzer tools, such as Wireshark, can capture Bitcoin protocol traffic by listening on the network to port and building a profile of transaction flow between IP addresses and Bitcoin addresses over time.
This is known as public key profiling. This method has weaknesses, such as the potential of Bitcoin addresses to change as frequently as every transaction. If this is the case, it will result in weak linkages to any network observations. Due to the peer-to-peer propagation of transactions any observation of an IP address where a transaction is intercepted may not be the original creator of the transaction.
This further removes any ability to reveal identity via Bitcoin address usage analysis on the network Turner and Irwin, Furthermore, Irwin and Turner , highlight the lack of reliability in this analysis approach and the inhibitors of revealing any illicit transaction. Nakamoto designed the Bitcoin system so that actors are pseudonymous. In addition, the transaction packet moving through the Bitcoin network does not contain the IP address.
Only transaction IDs are ultimately stored on the blockchain. The transaction payload is publicly available for anyone to view at any time on the blockchain.
Along with the transaction amount and timestamps, this payload reveals a concatenation of public keys. This comprises of the Bitcoin address and cryptographic signatures to provide an index linking the sender to the intended recipient of the Bitcoin Nakamoto, Other analysis challenges exist as presented by cyber security researcher Kaminsky, in a Black Hat presentation on Bitcoin security when the Tor application is used.
IP address obfuscation is achieved using a Tor router Onion Router. IP address and Bitcoin address mappings are lost, and any investigator will only find the IP address associated to a Tor exit node preventing any meaningful analysis Kaminsky, Considering the limitations observed at the network layer when analyzing illicit Bitcoin activity, the next section reviews the literature relating to graph data models and how nodes and relationships formed on the Bitcoin network can provide insight into illicit activity.
The ability to break the entire Bitcoin graph into two smaller DAGs was researched by Reid and Harrigan as they investigated the problem of anonymity. A second DAG represented the analysis of transactions over time. The second DAG represented a transaction as a node and the directed edges between Bitcoin source and target were modeled as the output of one transaction to the input of another, creating a transaction chain.
The graph may reveal transactions repeatedly performed by identifiable communities multiple entities or multiple transactions conducted by a single entity. Breaking the Bitcoin system down into two DAGs enables the ability to map and cluster behaviors of Bitcoin users and transactions over time.
Reid and Harrigan break the Bitcoin system into analyzable user and transaction graphs and apply their method to reveal identity by using multiple sources of data. These data sources include: Off network information building a directory of Bitcoin users which allows monitoring activity, common transaction usage and routing behavior, using a website called the Bitcoin Faucet 2.
This could ultimately be flawed due to the Bitcoin propagation protocol where the last routed Bitcoin node IP address is not necessarily where the transaction originated. Examples of where the Bitcoin Faucet system has been applied include, looking at address pattern behavior attributed to known entities, such as WikiLeaks.
In addition, using flow and temporal analyses to build a case study of Bitcoin theft. Taking algorithmic network analysis another step further helps the reader understand the evolutionary behavior of Bitcoin transactions and the way Bitcoin addresses adapt over time. Furthermore, advanced analytical techniques involving machine learning, can be used to determine the identity underneath the pseudonymous nature of Bitcoin addresses.
Ron and Shamir , provide a step in this direction by analyzing a graph of the largest transactions in Bitcoin through a series of sub-graphs, identifying multiple characteristic behaviors for the flow of Bitcoin transactions.
These patterns can be used to reflect common practice among users that may lead to suspicious behaviors on the Bitcoin network and these patterns can be re-used and applied to other illicit transaction scenarios.
The BlueNoroff cryptocurrency hunt is still on
This comprehensive overview of analysis techniques for illicit Bitcoin transactions addresses both technical, machine learning approaches as well as a non-technical, legal, and governance considerations. We focus on the field of ransomware countermeasures to illustrate our points. This paper examines the current literature on the analysis of illicit Bitcoin transactions and focuses specifically on the analytic techniques that are applied to blockchain data. These illicit Bitcoin transactions could take the form of money laundering, terrorism financing or the movement of proceeds from other crimes such as ransomware attacks.
When to trade bitcoin? When Saturn crosses Mercury, of course
The course begins with a brief history of money and overview of this new paradigm. The course aims to highlight the value proposition and differences relative to traditional finance. The remainder of the course involves practical activities and experimentation with DeFi applications and protocols. Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations. The Business School expects that you are familiar with the contents of this course outline and the UNSW and Business School learning expectations, rules, policies and support services as listed below:. Further information is provided in the Assessment and Policies and Support sections. Students may not circulate or post online any course materials such as handouts, exams, syllabi or similar resources from their courses without the written permission of their instructor. The Course Learning Outcomes CLOs are what you should be able to demonstrate by the end of this course, if you participate fully in learning activities and successfully complete the assessment items.
FINS5547 Cryptocurrency and Decentralised Finance - 2022
Other sections. A mysterious group with links to Lazarus and an unusual financial motivation for an APT. The group seems to work more like a unit within a larger formation of Lazarus attackers, with the ability to tap into its vast resources: be it malware implants, exploits, or infrastructure. See our earlier publication about BlueNoroff attacks on the banking sector. Also, we have previously reported on cryptocurrency-focused BlueNoroff attacks.
What is blockchain certification?
Research projects in the group focus on various aspects of network and computer security. In particular the group focuses on applications of cryptography to real-world security problems. For more information follow the links below. Integer lattices have found many applications in cryptography: in proofs of security , in attacks , and in constructing cryptosystems. Can multi-user services operate without sending all user data to the cloud in the clear?
WIPO Standards Workshop on Blockchain
This blog is an excerpt from the Chainalysis Crypto Crime Report. Click here to download the full document! So, how do criminals do it? Binance and Huobi lead all exchanges in illicit Bitcoin received by a significant margin. That may come as a surprise given that Binance and Huobi are two of the largest exchanges operating, and are subject to KYC regulations. How can they be receiving so much Bitcoin from criminal sources? Overall, just over , individual accounts at Binance and Huobi received Bitcoin from criminal sources in Are any of them significant traders?
Help us translate the latest version. Page last updated : January 31, This introductory paper was originally published in by Vitalik Buterin, the founder of Ethereum , before the project's launch in
Libertarians love its security and anonymity. Investors hope it will make them a killing. And people in some developing nations trust it more than their national currency. But is it really the future of money? C ryptocurrencies also known as cryptoassets or digital currencies were long treated as novelties.
The crypto economy worldwide has experienced significant milestones, fuelling the record surge of the digital asset, and the industry is expected to maintain momentum despite the fluctuations in its value. The first and second quarters of were punctuated by noteworthy developments in the field of cryptocurrencies, wherein the crypto market not only attracted retail investors, but also traditional financial institutions and large corporations that are looking to profit from the emerging trend of digital assets. The world is experiencing the greatest appreciation of cryptocurrency in history, and it is becoming clear that this will not be going away anytime soon. Africa is no exception, and Kenya is one of the three largest Bitcoin markets in Africa alongside giants like Nigeria and South Africa. This was aptly captured by firms like LocalBitcoins, a P2P Bitcoin marketplace that facilitates over-the-counter trading of local currency for Bitcoins.
Andrew Miller soc illinois. They promise to create new disruptive markets, and revolutionize how we think of money and financial infrastructure. The goal of this course is to introduce students to current research in cryptocurrencies. The bulk of the course will consist of reading and discussion of recent research papers from top security conferences.
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