The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention
- URL: http://arxiv.org/abs/2405.19762v1
- Date: Thu, 30 May 2024 07:17:57 GMT
- Title: The Kosmosis Use-Case of Crypto Rug Pull Detection and Prevention
- Authors: Philipp Stangl, Christoph P. Neumann,
- Abstract summary: Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks.
We propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Current methods to prevent crypto asset fraud are based on the analysis of transaction graphs within blockchain networks. While effective for identifying transaction patterns indicative of fraud, it does not capture the semantics of transactions and is constrained to blockchain data. Consequently, preventive methods based on transaction graphs are inherently limited. In response to these limitations, we propose the Kosmosis approach, which aims to incrementally construct a knowledge graph as new blockchain and social media data become available. During construction, it aims to extract the semantics of transactions and connect blockchain addresses to their real-world entities by fusing blockchain and social media data in a knowledge graph. This enables novel preventive methods against rug pulls as a form of crypto asset fraud. To demonstrate the effectiveness and practical applicability of the Kosmosis approach, we examine a series of real-world rug pulls from 2021. Through this case, we illustrate how Kosmosis can aid in identifying and preventing such fraudulent activities by leveraging the insights from the constructed knowledge graph.
Related papers
- The Latency Price of Threshold Cryptosystem in Blockchains [52.359230560289745]
We study the interplay between threshold cryptography and a class of blockchains that use Byzantine-fault tolerant (BFT) consensus protocols.
Existing approaches for threshold cryptosystems introduce a latency overhead of at least one message delay for running the threshold cryptographic protocol.
We propose a mechanism to eliminate this overhead for blockchain-native threshold cryptosystems with tight thresholds.
arXiv Detail & Related papers (2024-07-16T20:53:04Z) - Enhancing Trust and Privacy in Distributed Networks: A Comprehensive Survey on Blockchain-based Federated Learning [51.13534069758711]
Decentralized approaches like blockchain offer a compelling solution by implementing a consensus mechanism among multiple entities.
Federated Learning (FL) enables participants to collaboratively train models while safeguarding data privacy.
This paper investigates the synergy between blockchain's security features and FL's privacy-preserving model training capabilities.
arXiv Detail & Related papers (2024-03-28T07:08:26Z) - Graph Attention Network-based Block Propagation with Optimal AoI and Reputation in Web 3.0 [59.94605620983965]
We design a Graph Attention Network (GAT)-based reliable block propagation optimization framework for blockchain-enabled Web 3.0.
To achieve the reliability of block propagation, we introduce a reputation mechanism based on the subjective logic model.
Considering that the GAT possesses the excellent ability to process graph-structured data, we utilize the GAT with reinforcement learning to obtain the optimal block propagation trajectory.
arXiv Detail & Related papers (2024-03-20T01:58:38Z) - Generative AI-enabled Blockchain Networks: Fundamentals, Applications,
and Case Study [73.87110604150315]
Generative Artificial Intelligence (GAI) has emerged as a promising solution to address challenges of blockchain technology.
In this paper, we first introduce GAI techniques, outline their applications, and discuss existing solutions for integrating GAI into blockchains.
arXiv Detail & Related papers (2024-01-28T10:46:17Z) - Transaction Fraud Detection via an Adaptive Graph Neural Network [64.9428588496749]
We propose an Adaptive Sampling and Aggregation-based Graph Neural Network (ASA-GNN) that learns discriminative representations to improve the performance of transaction fraud detection.
A neighbor sampling strategy is performed to filter noisy nodes and supplement information for fraudulent nodes.
Experiments on three real financial datasets demonstrate that the proposed method ASA-GNN outperforms state-of-the-art ones.
arXiv Detail & Related papers (2023-07-11T07:48:39Z) - Chainlet Orbits: Topological Address Embedding for the Bitcoin
Blockchain [15.099255988459602]
Rise of cryptocurrencies like Bitcoin, which enable transactions with a degree of pseudonymity, has led to a surge in various illicit activities.
We introduce an effective solution called Chainlet Orbits to embed Bitcoin addresses by leveraging their topological characteristics in transactions.
Our approach enables the use of interpretable and explainable machine learning models in as little as 15 minutes for most days on the Bitcoin transaction network.
arXiv Detail & Related papers (2023-05-18T21:16:59Z) - Blockchain Large Language Models [65.7726590159576]
This paper presents a dynamic, real-time approach to detecting anomalous blockchain transactions.
The proposed tool, BlockGPT, generates tracing representations of blockchain activity and trains from scratch a large language model to act as a real-time Intrusion Detection System.
arXiv Detail & Related papers (2023-04-25T11:56:18Z) - Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application
of Machine Learning-based Forensics [5.617291981476445]
The paper analyzes a real-world dataset of Bitcoin transactions represented as a directed graph network through various techniques.
It shows that the neural network types known as Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) are a promising AML/CFT solution.
arXiv Detail & Related papers (2022-06-07T16:22:55Z) - Reputation-based PoS for the Restriction of Illicit Activities on
Blockchain: Algorand Usecase [2.94824047753242]
In recent times, different machine learning-based techniques can detect such criminal elements based on blockchain transaction data.
We propose a reputation-based methodology for response to the users detected carrying out the aforementioned illicit activities.
arXiv Detail & Related papers (2021-12-21T07:32:22Z) - Blockchain Phishing Scam Detection via Multi-channel Graph
Classification [1.6980621769406918]
Phishing scam detection methods will protect possible victims and build a healthier blockchain ecosystem.
We defined the transaction pattern graphs for users and transformed the phishing scam detection into a graph classification task.
The proposed multi-channel graph classification model (MCGC) is more able to detect potential phishing by extracting the transaction pattern features of the target users.
arXiv Detail & Related papers (2021-08-19T02:59:55Z) - Identity Inference on Blockchain using Graph Neural Network [5.5927440285709835]
Identity inference, which aims to make a preliminary inference about account identity, plays a significant role in blockchain security.
We present a novel approach to analyze user's behavior from the perspective of the transaction subgraph, which naturally transforms the identity inference task into a graph classification pattern.
We also propose a generic end-to-end graph neural network model, named $textI2 textBGNN$, which can accept subgraph as input and learn a function mapping the transaction subgraph pattern to account identity.
arXiv Detail & Related papers (2021-04-14T00:15:38Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.