Rug Pull Detection Using machine learning

Mohammed Arbaz, Jenil parjapati

Department of Biochemistry and Forensic Science. Gujarat University, Navrangpura, Ahmedabad. -380001

Email: ansariarbaz2004@gmail.com

            Jaypanchalxyz@gmail.com  

Abstract

The paper presents guidelines for data acquisition and analysis of market information related to cryptocurrency. Through RESTful APIs organizations access blockchain-transactions allowing them to retrieve both current and archived data for dependable and expandable operations. Data cleansing with various techniques coupled with value normalization methods together with significant financial and behavioral indicator extraction methods resulted in better data quality. The chosen set of indicators includes maximum drawdown, recovery ratio and total variation statistics. Users can use the identified indicators to inspect directional market patterns along with risk patterns and volatility measures. This system implements machine learning predictions alongside these features to detect both suspicions in funds movement and fraudulent trading activities and unusual trading patterns. Analyzing “real-world crypto data” serves as the foundation of our “rug pull” event detection because such information helps us recognize fraudulent payments with harmful trading activities. This document explains how combining financial expertise with machine learning enhances security levels and establishes cryptocurrency transparency and its results support both blockchain investigation and fraud detection and market risk management for DeFi platform security.

INTRODUCTION

The cryptocurrency market grows swiftly through daily accumulation of large transaction data. The decentralized finance (DeFi) platforms alongside blockchain networks execute millions of transactions which make fraud identification and risk assessment processes progressively complex. Blockchain decentralization and pseudonym usage makes it difficult for codes to detect activities like rug pulls and wash trading and pump-and-dump scams. Blockchain systems face detection challenges because they maintain operations without relying on involvement from third parties. Real-time transaction monitoring and anomaly detection systems with automated capabilities are in high demand due to increasing needs in the market.

The research presents an automatic system which employs machine learning to evaluate blockchain transactions through their entirety starting from data collection up to fraud identification. Remote procedure calls (RPCs) allow the methodology to collect present-day and archival transaction information from blockchain helper programs and decentralized trading systems. The subsequent analysis requires processed information that has undergone cleaning and normalization and standardization steps for proper data comprehension. The identification of fraudulent behavior through patterns derived from risky trading activities becomes feasible because of engineered features including Maximum Drawdown (MD), Recovery Ratio (RR) and Total Variation (TV). These features enable the model to detect rug-pull schemes together with other illicit market activities with precision.

The proposed system improves cryptocurrency market security together with transparency because it can detect fraud while performing real-time risk assessments. Experimental findings prove how this system detects fraudulent transactions effectively which establishes it as an excellent market analysis tool. The research method groups different sections according to Section 2 which examines relevant blockchain fraud detection and risk assessment literature and Section 3 shows the data acquisition and preprocessing procedures. Next the experimental framework together with evaluation parameters are described in Section 4 before results analysis occurs in Section 5. Section 6 of the paper offers final conclusions about blockchain fraud detection research before proposing research directions for the future.

RELATED WORK

Most financial market research has analyzed time series data [1] and employed machine learning to identify anomalous patterns [2]. The research findings demonstrate that fraudulent acts surface regularly.

The identification of atypical trading patterns together with irregular volume fluctuations and strange market activities allows fraud detection. Blockchain transaction data lacks sufficient research about its specific features which demand custom investigation methods because of its decentralized nature and market fluctuations along with regulatory challenges. Blockchain records prove different from conventional systems because they provide immutable architecture as well as transparent features together with pseudonymous characteristics. These beneficial qualities of blockchain operations introduce obstacles alongside their benefits for detecting fraud. Smart contracts are crucial to platforms like Ethereum and the broader DeFi ecosystem, but they can sometimes contain vulnerabilities that lead to security issues [16].As these contracts operate under the principle that “code is law,” unnoticed bugs can result in serious exploits, financial losses, or unauthorized withdrawals.To address these risks, researchers have developed automated tools [8,9] that audit smart contract code for common mistakes, reentrancy issues, and logic errors that could be exploited by malicious actors.However, these tools are not flawless, as new attack strategies continue to evolve and expose unforeseen vulnerabilities. Unauthorized tokens are presently becoming major issues that extend beyond conventional contract usage. The purpose of fake tokens is to fool investors as they enable criminal operations which include money laundering [17,18] and rug pulls and pump-and-dump frauds and Ponzi schemes. The permissionless blockchain environment enables scammers to construct tokens without following any regulatory standards.

scrutiny. Two methods have helped security by enabling advance identification of suspicious token activity through risk assessment approaches [19] and machine-learning-based system detection of fraudulent behavior. Fraud analytics models based on blockchain monitoring together with transaction network assessments and liquidity monitoring perform identification of fraudulent tendencies.Cryptocurrency Scams

Rug pulls stand as the principal scams which occur frequently within decentralized finance (DeFi). The illicit act of liquidity extraction occurs when developers take away all project funds suddenly thus making investors retain nothing but useless tokens.

tokens. Scammers exploit fake advertising together with false partnerships and their fake promises to dupe investors.

claims to attract unsuspecting investors. Research reveals that 97% of Uniswap V2 tokens prove malicious [15].

Most cryptocurrency scams involve malicious actions while rug pulls occur often as a deceptive practice. Blockchain forensics tools now exist to monitor the activities and transactions of developer wallets while following token distribution patterns in such monitoring systems. The techniques focus on generating advance detection systems through

The analysis detects abnormal activities related to withdrawals and trading through automated systems. Scammers execute pump-and-dump schemes as one of the most prominent forms of deception through which they artificially increase token prices.

They create artificial market hype or pumping to increase the price of a token before selling their stake for profit or dumping thus leading to price collapse. The nature of these scams focuses on trading tokens which exhibit poor liquidity levels since these tokens become susceptible to social media manipulation and coordinated trading operations.

Scientists have established machine learning algorithms for identifying and detecting pump-and-dump fraud activity on Binance. Research involving cryptocurrency price changes alongside evaluation of social network sentiments and platform activities enables preventive measures for detecting fraud incidents before they happen due to the decentralized anonymity of trading conditions.

Ongoing enhancement of fraud prevention models is required due to the evolving nature of the cryptocurrency Ponzi schemes in the market. Scammers operate by providing unrealistic promising returns to initial investors who feed the scheme using new participant funds for payment.

funds from newer participants. The carrying out transactions with cryptocurrencies through decentralized systems with pseudonymous features enables scammers to run undetected. Users can detect Ponzi schemes through the analysis of smart contract code since these schemes usually block fund withdrawals. The development team uses automated security auditing tools to identify vulnerabilities before system deployment according to research in [8] and [9]. Security tools inspect the code in smart contracts to detect several vulnerabilities which unethical users could abuse. New attack strategies force automated tools to operate under imperfect conditions because they cannot prevent emerging vulnerabilities.

The number of security risks stemming from legitimate smart contracts has risen while malicious tokens have become an expanding danger to blockchain systems. Speculators create fraudulent tokens as a method to trick investors and enable money laundering along with running activities like rug pull and Ponzi schemes and pump-and-dump schemes. The permissionless Bitcoin blockchain creates an opportunity for scammers to create tokens without any government monitoring. Risk assessment methods together with machine learning-based fraud detection models help security through early detection of token suspicious activities that stop investors from becoming scam victims according to research findings [19]. The analytical methods of blockchain combine transaction analysis with liquidity trackin to find unusual signs that indicate fraud.

In the cryptocurrency universe rug pulls stand as one of the major frauds that specifically affect the decentralized finance platform. The fraudulent event known as rug pull emerges when project developers steal all project liquidity which results in investors losing their entire investment value. Thieves resort to manufacturing deceptive advertising campaigns combined with false partnership promotions together with inflated statements to trick innocent investors. Researchers in [15] discovered that malicious tokens dominate Uniswap V2 to the extent of 97% while rug pulls represent the most common form of DeFi scams. Blockchain technology uses forensic methods to monitor wallet addresses of developers as well as track transaction flows and analyze token distribution. Early warning systems achieve their function through detecting both unusual liquidity withdrawals and atypical trading activities. People who conduct token price manipulations create artificial market hype for increasing prices (pumping) which they later cash out (dumping) leading to swift price decreases. Such schemes operate mainly on low-liquidity tokens by means of social media promotion and strategic trading between participants. Researchers have analyzed machine learning approaches to identify pump-and-dump activities within the Binance platform which have shown acceptable outcomes according to [15]. Through the assessment of market prices together with social media sentiments and network interaction patterns these methods identify impending pump-and-dump activities ahead of their execution. The enforcement of cryptocurrency trading becomes difficult because of its decentralized structure along with the anonymity it provides while additional improvements continue to be made to prevent fraud detection.

The age-old financial crime known as Ponzi schemes has adapted itself to operate within the cryptocurrency market. The perpetrators of such schemes utilize newer investors’ funds to provide high returns for existing investors. Due to their predefined nature crypto systems enable scammers to create false identities for evading discovery. Smart contracts enable analysis of Ponzi schemes by allowing examination of code that denies users the ability to withdraw funds since these schemes differ from rug pulls and pump-and-dump schemes. Research shows that machine learning analysis successfully detects monetary fraud patterns in negotiation systems while observing contract interactions and transaction patterns [12/11].

The identification of Ponzi schemes through code audits is possible but rug pulls and pump-and-dump scams evade detection during code analysis because these scams heavily depend on social engineering methods and market manipulation attempts. Time series analysis in combination with machine learning algorithms demonstrate their capacity to identify fraudulent tokens through an investigation of transaction patterns together with liquidity flow as per research conducted in 2015 [15]. The analysis of time series characteristics enabled researchers to detect Uniswap V2 rug pull tokens right after token launch within 24 hours which enhanced quick detection capabilities.

The models use anomaly detection besides clustering algorithms alongside transaction graph embeddings to recognize fraudulent tokens from ordinary ones.

The improvement of fraud detection systems has not eliminated the need to analyze fraud risks across multiple time periods. An investor who exchanges token A for token B requires thorough evaluation of B’s risk for a rug pull since token B is new to them and unavailable in trading records. Our research examines different time intervals starting from 20% to 90% of a token’s existence to establish better accuracy in rug pull detection methods.

Our research eliminates the analysis of malware threats and crypto cybercrimes such as ransomware and phishing and token generator scams which existing work primarily addressed [21, 22, 23, 24 and 25]. We direct our research toward decentralized financial frauds especially rug pulls and pump-and-dump schemes through a data-driven methodology which improves live market risk assessment for cryptocurrencies. The presented work tries to unite traditional financial fraud detection methods with blockchain-based security through blockchain analysis together with intelligent smart contract verification and predictive system development to produce enhanced DeFi protection systems.

DATA COLLECTION

The Etherscan API enables us to acquire blockchain transaction information for ERC-20 tokens as our data collection process employs this public API to examine fraud patterns in decentralized finance. The collection process extends beyond one operation since it requires us to obtain token metadata alongside transaction history data followed by structured storage for analysis purposes. The complete system operates according to Algorithm 1 which follows below.

The dataset functions to invoke transactions from Etherscan API to retrieve token transaction histories while the API uses token contract addresses to get limited recent transaction data ordered by most recently executed actions first. The transaction entries receive token metadata to store essential information about token names and symbols. The analysis-ready data receives storage as CDV for future assessment purposes. The application waits during API rate limit situations to allow successive requests. A properly designed data collection process respects the guidelines established by API providers. A system that handles missing or incomplete transaction data has been established within the data collection pipeline through data validation and error filtering processes to maintain proper information quality. The assembled data consists of token transaction data linked with significant metadata that allows efficient study for researchers.

Early identification of decentralised finance DeFi fraud activities such as rug pulls along with pump-and-dump schemes is possible through the analysis of this dataset using machine learning methods.

DATA PREPROCESSING AND FEATURE ENGINEERING

Multiple preprocessing steps must be conducted prior to extracting features because they make blockchain transaction data reliable for analysis and make it consistent and easy to understand. Extensible fraud detection requires standardized data formats because the blockchain transactions currently exist in multiple different ways. During preprocessing the timestamp data gets converted while values get normalized through proper timestamp sorting. Blockchain transactions record timestamps with UNIX timestamps that get converted into standard date and time formats for chronological sorting and extraction of time-based features. The preprocessing workflow normalizes ERC-20 tokens by standardizing their transaction values regardless of their decimal value specifications. A system for timestamp sorting enables users to achieve consistent chronological ordering because this method proves vital for trend analysis alongside fraud detection applications.

The process of feature engineering becomes important after preprocessing since it finds irregularities and fraud patterns in decentralized finance (DeFi) transactions. Rolling window methods generate three essential metrics namely Maximum Drawdown (MD), Recovery Ratio (RC) and Total Variation (TV). The Maximum Drawdown metric reveals the biggest value decline of a token enabling detection of exit scams and significant market sell-offs. Recovery Ratio measures the ability of a token to rebound when values decrease thus offering insight into market stability levels. Total Variation provides insight into sudden trading volume changes because these shifts sometimes originate from price manipulation activities or drastic sell-offs.

The analysis gets additional strength from additional derived metrics that build upon core indicators. Mean Transaction Value determines a transaction value average over a specified time span to monitor sudden changes in buying or selling intensity. The examination of Transaction Frequency allows analysts to determine how many trades happen in designated time periods because frequent trading might reveal evidence of pump-and-dump operations. The standard deviation measurement of transaction values in Liquidity Volatility identifies the abnormal trading patterns that result from price manipulation followed by market collapses.

Preprocessing and feature engineering operations transform blockchain data into a structured format which develops a strong tool dedicated to fraud detection. This system provides an extensive fraud detection capability through financial risk metrics together with transaction behavior indicators alongside volatility measures. The methodology results in better risk evaluation throughout DeFi infrastructure which enables the identification of fraudulent practices including rug pulls and pump-and-dump operations and liquidity manipulation.

METHODOLOGY

A developed system combines data extraction with processing and mining steps to develop an effective mechanism for pointing out DeFi financial scams. Automated blockchain data transformation enables the system to extract financial metrics which detect unusual behavior associated with potential fraud. A defined operational architecture separates the method into four stages including data acquisition followed by preprocessing after which feature engineering takes place before fraud detection modeling. The system analyzes transactions in an organized manner to keep markets stable while also detecting sudden market manipulations. Detection methods employ mathematical models for calculating financial risk indicators through computation of Maximum Drawdown (MD) and Recovery Ratio (RC) and Total Variation (TV). The proposed methods establish standardized procedures to inspect market transaction sequences and find fraudulent activity in market activity. Maximum Drawdown quantifies the biggest value drop a token experiences during an observation period and detects both rug pulls and liquidity issues. It is mathematically defined as:

MD                                 ………………………….. (1)

where 𝑋 max ​ is the maximum value of transaction within the sliding observation period. 𝑋 min ​ denotes the minimum transaction value. MD metric allows for identifying sharp losses in asset prices that are most likely to be related to fraudulent activities in the DeFi sector. The Recovery Ratio (RC) indicates how much the bull runs impact the price of a token. The smaller the RC value, the more likely it is that an asset has failed to recover and may be a case of abandoned or failed projects. The RC is computed as:

……………………….. (2)

where 𝑋s ​ The last transaction value among the rolling window is is. 𝑋 max and this is the highest transaction value. 𝑋 min X min ​ is the lowest transaction value. Values close to 1 represent strong recovery ratios and values close to zero poor ones. In addition, we utilize Total Variation (TV) to measure fluctuations on transaction values by taking absolute changes within a rolling window. It is defined as:

……………………………. (3)

Making sure the measure is made normalized to the value of the whole. On a Total Variation reading that is high, it often signals fraudulency in pump and dump schemes or sudden liquidity shift. The next phase of research will leverage these extracted financial risk indicators to create machine learning models that predict the token is fraudulent or not fraudulent. By identifying suspicious market manipulations, and by enhancing the security in the DeFi landscape, there exists a significant improvement in the reliability of fraud detection on decentralized ecosystems proposed.

EXPERIMENTS AND RESULTS

We validated our approach using an experimental analysis of con macro transactions from ERC-20 token blockchain dataset which contained number of thousands transactions. The motivation was to quantify how well the transaction irregularities could be detected by the methods based on Maximum Drawdown (MD), Recovery Ratio (RC), and Total Variation (TV). These financial risk indicators were used to mine for suspicious transaction patterns in the fraud detection system. The dataset was splitted into 80% splitted for training and 20% for testing for the experimental setup. For classification purposes, there were two machine learning models used: Random Forest (RF) model is an ensemble learning model that group multiple decision trees together to aggregate predictions to improve the classification results, and it is a Support Vector Machine (SVM) model which classifies fraudulent transactions from legitimate ones using hyperplanes. A feature selection method was used to identify the most important variables to improve efficiency as well as accuracy during model training. In the results, the extracted features increased the accuracy of fraud detection greatly. As displayed in Table 1, the technique is able that it would effectively discern fraudulent transactions from genuine commercial activities for recognition in decentralized finance (DeFi) environments.

Classifier Performance:

The predicted transaction pattern presented a highest accuracy level of 92.3% using a Random Forest classifier due to ensemble learning structure which is a predictive method. Similar performance was shown but marginal decrease for complex transaction patterns on the Support Vector Machine (SVM). The impact of detection of fraud relies on these financial risk indicators such as Maximum Drawdown (MD), Recovery Ratio (RC), and Total Variation (TV). The extracted features performed very well in separating genuine and fraudulent ones, making them potentially well suited for security applications in real world. Future research can explore to deeper models such as the deep learning to further increase the detection performance and in particular to handle more sophisticate fraud strategies in decentralized markets.

DISCUSSION

The proposed information processing structure uses several stages beginning with data acquisition followed by transformation and subsequent superior feature creation. Systematic analysis of financial patterns with transaction data enables the detection of abnormal monetary activities that help lower the risk of cryptocurrency market fraud such as rug pulls. The experimental results validate that the chosen characteristics work extremely well when identifying abnormal patterns in decentralized finance networks. There remains space to advance the method further. More financial indicators together with blockchain-specific metrics must be incorporated for future attempts at improving feature extraction methods. The application of Long Short-Term Memory (LSTM) networks alongside Graph Neural Networks (GNNs) and Transformer-based architectures would enable better accuracy since they process time-based transaction patterns effectively. A dynamic cryptocurrency environment can be detected better by real-time anomaly detection systems which integrate with adaptive learning procedures in the framework.

CONCLUSION

By using an established data-oriented system researchers gained better capacity to monitor for fraud through analyzing cryptocurrency transactions. This process builds an effective anomaly detection system structure based on machine learning methodologies in a sequential order. The research showed that MD Variable (MD) and Recovery Ratio (RC) together with Total Variation (TV) successfully identified unlawful activities within Decentralized Finance networks through experimental tests. The framework shows effective application in practical settings because it successfully supports evaluating risks and prevents fraud and meets regulatory standards. Security improvements for blockchain threat detection systems will emerge from enhanced artificial selection of features and deep learning implementations and real-time threat detection abilities.

ACKNOWLEDGMENT

The authors thank the Etherscan API contributors who granted transaction data access because their data proved essential to accomplish this research. They give credit to the developers who created open-source libraries Pandas and NumPy because their tools made it possible to preprocess data and extract features as well as perform analyses. The research gained efficiency and accuracy through these resources. Such tools provided critical support for creating an effective fraud detection system within decentralized finance (DeFi) networks.

REFRENCES