Bitiodine extracting intelligence from the bitcoin network power
Blockchain is a decentralized transaction and data management technology developed first for Bitcoin cryptocurrency. The interest in Blockchain technology has been increasing since the idea was coined in The reason for the interest in Blockchain is its central attributes that provide security, anonymity and data integrity without any third party organization in control of the transactions, and therefore it creates interesting research areas, especially from the perspective of technical challenges and limitations. In this research, we have conducted a systematic mapping study with the goal of collecting all relevant research on Blockchain technology. Our objective is to understand the current research topics, challenges and future directions regarding Blockchain technology from the technical perspective.
We are searching data for your request:
Bitiodine extracting intelligence from the bitcoin network power
Upon completion, a link will appear to access the found materials.
Content:
- Boot Up: Ellison era ends, Toshiba PC retreat, Apple's missing canary
- Detecting malicious accounts in permissionless blockchains using temporal graph properties
- Altcoin Mining Toggled On AWS Spot Rate : BitcoinMining
- Forensic Analysis Publications
- Analyzing the Bitcoin Network: The First Four Years
- How should Bitcoin be regulated ?
Boot Up: Ellison era ends, Toshiba PC retreat, Apple's missing canary
Applied Network Science volume 6 , Article number: 9 Cite this article. Metrics details. Directed Graph based models of a blockchain that capture accounts as nodes and transactions as edges, evolve over time.
This temporal nature of a blockchain model enables us to understand the behavior malicious or benign of the accounts. Predictive classification of accounts as malicious or benign could help users of the permissionless blockchain platforms to operate in a secure manner. Motivated by this, we introduce temporal features such as burst and attractiveness on top of several already used graph properties such as the node degree and clustering coefficient. Using identified features, we train various Machine Learning ML models and identify the algorithm that performs the best in detecting malicious accounts.
We then study the behavior of the accounts over different temporal granularities of the dataset before assigning them malicious tags. For the Ethereum blockchain, we identify that for the entire dataset—the ExtraTreesClassifier performs the best among supervised ML algorithms. On the other hand, using cosine similarity on top of the results provided by unsupervised ML algorithms such as K-Means on the entire dataset, we were able to detect more suspicious accounts. Further, using behavior change analysis for accounts, we identify unique suspicious accounts across different temporal granularities.
A blockchain can be modeled as an ever-growing, large directed temporal network with more and more industries starting to adopt it for their businesses. In permissionless blockchains, interactions also called as transactions happen between different types of accounts. Here, transactions from an EOA called as an external transaction are recorded on the blockchain ledger whereas transactions from an SC called as an internal transaction are not recorded on the ledger.
Note that in Ethereum, SCs can issue external transactions by executing external calls also which would be recorded in the blockchain ledger. With actual money involved in most of the permissionless blockchains, an account must be able to perform secure transactions. Recently, many security threats to various blockchain platforms have been identified Bryk For some identified vulnerabilities, counter-measures have been implemented.
We do not delve into surveying all the security threats. In Chen et al. In many of the security vulnerabilities identified in Ethereum blockchain, hackers target other accounts by either hacking SCs or implementing malicious SCs for cyber-crimes such as ransomware, scams, phishing, and hacking of exchanges or wallets Chainalysis With an ever-increasing growth and adoption of the blockchain technology by the industry and the crypto-currency markets, permissionless blockchains are at the epicenter of increased security vulnerabilities and attacks.
Our motivation for this work is based on the fact that there is limited work on learning the behaviors of the accounts in permissionless blockchains which are malicious and potentially victimize other accounts in the future.
We define malicious accounts as those accounts that have been founded to be involved in illegal activities such as phishing, hacking, scams, and Ponzi schemes. Benign accounts as those that perform only legitimate transactions. Although, these illegal activities have different features, in this paper we assume that the transaction behavior and features are consistent and are applicable to all types of malicious activities.
The results seem to bear out such assumption. In short, in this paper, we aim to identify malicious accounts so that the potential victims and blockchains can deploy counter-measures.
In this paper, henceforth, the term blockchain is used to represent a permissionless blockchain. Nonetheless, the features used by the available techniques are: a limited and not learned from the previous attacks on blockchains, and b extracted from the aggregated snapshot of time-dependent transaction graphs that do not consider the temporal evolution of the graphs.
The temporal aspects attached to the features are essential in understanding the actual behavior of an account before we can classify it as malicious.
For example, inDegree and outDegree features are time-variant and should be considered a time series. Nonetheless, it has been proven that the aggregated node degree distribution for accounts follows a power-law in blockchains such as Ethereum Chen et al.
Here, questions that we ask are: does such behavior exist in all accounts? Is there a burst of degree for certain accounts at certain instances and can the existence of such bursts be used to identify malicious activity?
To answer these questions, we first identify the existence of bursts. Then, we introduce features such as temporal burst , degree burst , balance burst , and gasPrice burst to study the effect of bursts. The fat-tailed nature of power-law degree distribution also gives rise to neighbor-hood-based fitness preferential attachment in blockchains Aspembitova et al. In Aspembitova et al. Here, the authors define the fitness factor considering one previous time instance interactions.
As it does not consider a temporal window, one drawback of the method lies in its ability to correctly classify malicious transactions that appear at an interval of 2 time units or more.
Our attractiveness measure takes into account the stability of directed transactions that happened between two accounts in the past. Intuitively, a malicious account will have high attractiveness as it will tend to transact with new accounts while benign accounts will have high neighborhood stability or low attractiveness.
As the behavior of an account can change from malicious to benign or from benign to malicious over time, there is a need for continuous monitoring and analysis of the real-time transactions given the history of transactions performed by an account. We, thus, study the evolution of malicious behavior over different timescales by creating sub datasets and then answer the question: would a certain account show malicious behavior in the future? Towards this, we first apply different ML algorithms and identify the most suitable unsupervised ML algorithm for the entire dataset that is able to cluster accounts most accurately.
Then we apply the identified algorithm to different sub datasets within a temporal scale to capture the behavior changes. Feature engineering We identify feature vector for identifying malicious accounts based on previous attacks on blockchains and perform time series analysis.
As new features, we propose temporal burst , degree burst , balance burst , gasPrice burst , and attractiveness. Comparative analysis We perform a comparative study with techniques proposed in the related studies and identify the best possible supervised and unsupervised ML algorithm with related hyperparameters when we use Ethereum transaction data. Results Our results demonstrate that for the supervised case, ExtraTreesClassifier performs the best with respect to balanced accuracy for the entire dataset while for the unsupervised case, we are able to identify more suspect accounts using K-Means Clustering.
Analysis of the behavioral changes reveal suspects across different temporal granularities. The rest of the paper is organized as follows. In Sect. In Sects. This is followed by an in-depth evaluation along with the results in Sect.
We finally conclude in Sect. Further, in Abbreviation section we provide a glossary of the acronyms used in the paper. There are two types of blockchain technologies, permissionless and permissioned. The major difference between the two technologies is that in a permissioned blockchain prior access approval is needed for performing any action on the blockchain while in permissionless blockchain anyone can perform actions on the blockchain without any approval.
Further, there is no way to censor anyone from permissionless blockchains. Such aspects lead to more frauds and malicious activities to prevail in permissionless blockchains. Ethereum and Bitcoin use permissionless technology.
Ethereum was developed by Buterin and allows users to run programs in its trusted virtual environment known as Ethereum Virtual Machine EVM. These programs are called Smart Contracts SC and are stored on the ledger along with transactions performed on a given fixed address.
Smart Contracts SC can also send, store, and receive Ether. Once deployed, SC is a hard coded program that could only be fed with input to get output. SCs are also used by some applications for their processing.
Such applications are called distributed applications or dapps. Although Ethereum is known for its security and trust, a small bug in a SC code can cause huge loss of crypto-currency Atzei et al. Ethereum manages a list of accounts along with the account balance.
For a valid transaction usually amount is transferred from a sender to a receiver. If the receiver is an SC, its code is executed and the state of the SC is updated. Internally, a SC could send a message or perform internal transactions with other accounts. Ethereum currently uses a refined form of PoW Proof of Work consensus algorithm. PoW is computationally expensive and energy inefficient.
Moreover, Appendix 1 presents a brief overview of PoW. There are vast number of studies in fraud detection Abdallah et al. Nonetheless, targeting Ethereum, Chen et al. We do not survey all the attacks and defense mechanisms in this work. However, we provide an in-depth understanding of different methods used to detect accounts involved in malicious activity.
Several works have tried to identify or categorize malicious accounts and activities in different types of blockchains. As blockchains have a graph structure, most of these techniques study graph properties such as node degree to identify features before applying supervised or unsupervised learning. In Pham and Lee , authors used a bitcoin transaction network to detect malicious activity.
They were able to detect three malicious attacks using unsupervised ML algorithms with a limited amount of transaction data they had. In their followup work, they used a more comprehensive bitcoin transaction dataset starting from genesis block until April 7 th , Pham and Lee They employed data in two types of graphs namely User Graph and Transaction Graph.
In the user-graph, nodes represent accounts and edges represent transactions, whereas in the transaction-graph nodes represent transactions and edges represent the flow of bitcoins.
They first studied the flow of bitcoins to prove the existence of anomalies and then performed clustering to identify different attacks. Inspired by Pham and Lee , in Monamo et al. They validated their approach using trimmed K-Means , argued its usefulness in detecting anomalies, and detected 5 out of 30 fraudsters. In another bitcoin-related malicious activity detection, authors studied the detection of addresses involved in the Ponzi scheme Bartoletti et al.
They used supervised learning and validated their results after addressing the class imbalance that is inherent in any malicious activity related dataset. They identified that the Gini coefficient of outgoing Ethers and the ratio between incoming and total transactions are the most important features for detecting Ponzi scheme related accounts. In another Ponzi scheme related study, in Chen et al.
Their motivation behind the study was based on the fact that the opcodes reflect the logic implemented in a SC and therefore provide useful features for identifying Ponzi and non-Ponzi SC.
They also figured out that opcode based features are more efficient than account based features while detecting Ponzi scheme accounts. In Ostapowicz and Zbikowski , the authors use partial Ethereum transaction data to classify malicious accounts.
They also performed a sensitivity analysis to study the effect of different classifiers on the feature set.
Detecting malicious accounts in permissionless blockchains using temporal graph properties
Skip to content. Star 4. Permalink master. Branches Tags. Could not load branches. Could not load tags.
Altcoin Mining Toggled On AWS Spot Rate : BitcoinMining
Publications Bibtex About. Filter by Year Use the buttons below to only show publications of a given year. Peer Review Should we show just peer-reviewed publications? Peer-Review required. Or Better Get Real? Charles-Antoine Flament — 3. Lakshman — Digital Currencies: Beyond Bitcoin Hanna Halaburda — Blockchain technology -- applications in improving financial inclusion in developing economies : case study for small scale agriculture in Africa Malvern Chinaka — From Bitcoin to Smart Contracts: Legal Revolution or Evolution from the Perspective of de lege ferenda?
Forensic Analysis Publications
Larry Ellison has agreed to step down as chief executive officer at Oracle ORCL , ending one of the most entertaining and profitable runs for a leader in business history. Oracle announced Ellison's departure via a press release delivered on Thursday afternoon after the close of U. The company said that Ellison will remain chairman of Oracle's board and take on the role of chief technology officer. Catz will remain as chief financial officer as well.
Analyzing the Bitcoin Network: The First Four Years
Authors: Bharat Bhushan, Nikhil Sharma. Hassija, V. Chamola, V. Saxena, D. Jain, P.
How should Bitcoin be regulated ?
Tasca ucl. Ifthe input is larger than the new transaction output the client generates a new Bitcoin address, and sendsthe difference back to this address. This is known as change. From the Bitcoin wiki: Take the caseof the transaction 0a1c0b1ec0ac55a45bdaf2ef5bccddffef87, a The client cant spend just The entire The
Skip to search form Skip to main content Skip to account menu You are currently offline. Some features of the site may not work correctly. DOI: We also show that there is a gambling network that features many very small transactions.
While these clusters can remain largely anonymous, the authors are able to ascribe many of them to particular business categories by analyzing some of their specific transaction patterns TPs , as observed during the period from to The authors are then able to extract and create a map of the network of payment relationships among them, and analyze transaction behavior found in each business category. Four primary business categories are identified in the Bitcoin economy: miners, gambling services, black markets and exchanges. That is, a one-day effect where traders, gamblers, black market participants and miners tend to cash out on a daily basis.
Bitcoin is digital assets infrastructure powering the first worldwide decentralized cryptocurrency of the same name. All history of Bitcoins owning and transferring addresses and transactions is available as a public ledger called blockchain. But real-world owners of addresses are not known in general. However, some addresses can be grouped by their ownership using behavior patterns and publicly available information from off-chain sources. Blockchain-based common behavior pattern analysis common spending and one-time change heuristics is widely used for Bitcoin clustering as votes for addresses association, while offchain information tags is mostly used to verify results. In this paper, we propose to use off-chain information as votes for address separation and to consider it together with blockchain information during the clustering model construction step.
Embed Size px x x x x Bitcoin generation process is called Bitcoin Mining. Requires immense computational power from dedicated Bitcoin Miners connecting through internet protocols. Each Bitcoin block contains bitcoin transactions.
There are no comments yet.