Dongze Li, Kejia Zhang, Shiwei Li, Gang Du · 5 authors
As decentralized finance (DeFi) grows and decentralized exchanges (DEXs) expand, the security of Ethereum smart contracts and blockchain transactions is receiving increasing scholarly attention. The ERC20 token standard has facilitated the emergence of numerous honeypot contracts, which deceive traders by allowing token purchases but blocking withdrawals. This study proposes a lightweight honeypot contract detection system integrated into the go-Ethereum client (Geth). Unlike previous work, our detector does not rely on contract interaction records or source code provided by contract creators. Instead, our approach performs static data flow analysis on contract bytecode to identify honeypot mechanisms. By focusing exclusively on the control flow of the ERC20 Transfer method, our system achieves faster detection than full-contract analysis methods, with an average processing time of 9.74 milliseconds per contract. Experiments on both known honeypot contracts and real-world token contracts demonstrate the effectiveness of our approach in detecting malicious ERC20 contracts.
In the rapidly advancing domain of smart manufacturing, securing data integrity and preventing unauthorized access are critical challenges. This study introduces a novel approach that synergizes anomaly detection techniques with Zero-Knowledge Proofs (ZKPs) to fortify the security framework of smart manufacturing systems. Our methodology employs a combination of data preprocessing, including statistical imputation and data smoothing, alongside advanced anomaly detection using classification methods and neural networks, particularly focusing on deep learning architectures. The detected anomalies undergo verification through zk-SNARKs, a specialized ZKP scheme, ensuring a robust validation process without compromising data confidentiality. Our findings reveal a notable enhancement in the accuracy of anomaly detection, achieving detection rates of approximately 95% for temperature fluctuations and 90% for pressure irregularities, with a significant reduction in false positives. This performance is markedly superior to traditional methods and aligns closely with the highest efficacy rates reported in contemporary studies. Moreover, the utilization of ZKPs for anomaly verification demonstrated a 98% success rate, ensuring the secure and private verification of anomalies. The integration of anomaly detection with ZKPs presents a significant leap forward in addressing the security vulnerabilities inherent in smart manufacturing. This study not only showcases the effectiveness of our approach in enhancing data security and integrity but also sets a benchmark for future research in creating more resilient and trustworthy industrial operations.
Blockchain has emerged as a groundbreaking security technology, playing a vital role in various industries such as banking, the Internet of Things (IoT), healthcare, education, and voting. However, the widespread adoption of this technology has introduced certain vulnerabilities, particularly in the form of exploitation by malicious entities. While existing research primarily focuses on identifying anomalous actor behavior, there has been limited exploration of precisely identifying hostile actors within the Ethereum network. This study aims to uncover malevolent actors operating on the Ethereum network and categorize attacks based on their actions. To achieve this research goal, a new dataset was constructed by consolidating data on malicious actors involved in illicit Ethereum activities. Key features were extracted from this dataset using advanced feature selection techniques, including Principal Component Analysis (PCA), Information Gain, and Ridge Regression. Machine learning classifiers such as LGBM, XGBoost, Random Forest, Extra Tree, Bagging, and K-Nearest Neighbors were applied to identify and classify malicious actors effectively. The results, achieving an impressive accuracy rate of 98%, underscore the effectiveness of Information Gain when coupled with LGBM and XGBoost. Notably, XGBoost demonstrates efficiency by completing the analysis in a mere 13.72 seconds. In addition to identifying fraudulent activities, this research classifies them into distinct categories, enhancing blockchain security and addressing trust concerns. This study’s outcomes fortify the Ethereum network’s resilience and contribute to the broader discourse on bolstering reliability in blockchain systems.
The Internet of Healthcare Things (IoHT) is an emerging critical technology for managing patients’ health. They are prone to cybersecurity vulnerabilities because they are connected to the internet, primarily by wireless connections. This is a major concern, considering data privacy and security. Artificial intelligence (AI) models are excellent methods to detect and mitigate cybersecurity vulnerabilities. Since medical Information Technology (IT) is evolving and data privacy is a major concern with sensors generally, in healthcare IoT. The TON_IOT, Edge_IIoT, and UNSW-NB15 datasets were used in this study for assessment and implementation to solve the challenge using the chosen benchmark AI models with the integration of IPFS blockchain technology in order to decentralize and secure the data. Justifiable parameters were used to determine how efficient each technique is in predicting the best outcome. The results show the efficiency of the utilized models, particularly the Support Vector Machines (SVM). The TON_IoT dataset obtained 100% accuracy, the Edge_IIoT dataset obtained 98% accuracy, and the UNSW-NB15 dataset obtained 89% accuracy. The integrated blockchain technology in this model is applied for security purposes. Utilizing these techniques will proffer a secure and safe transmission of medical data. This study will generally provide important insight to other researchers in the healthcare field.
Smart contracts as one of the most successful applications of blockchain. It holds digital currency with huge economic value. During the rapid development of smart contracts, vulnerabilities in the contracts have caused huge financial losses to the blockchain. This has strengthened researchers’ focus on smart contract security vulnerability detection. In this paper, we explore a vulnerability detection deep neural network-based method on combining features in both contract source code and bytecode forms. We conduct extensive experiments on the Ethereum smart contract datasets for reentrancy vulnerability. The experiment demonstrates that our method achieves 87% accuracy and 78% f1-score. Another experiment shows that our method maintains a good detection performance even when the feature part is missing.
Amit Kumar, Neha Sharma, Rahul Chauhan, Manish Sharma
The implementation of blockchain technology has become prevalent across several businesses, although its initial design was primarily intended for cryptocurrencies. The preservation of security and integrity inside blockchain networks is of utmost importance, and the process of anomaly detection serves as a crucial component in the identification and mitigation of potential threats. This research study aims to examine the patterns within the Google BigQuery Bitcoin Blockchain Dataset and utilize unsupervised anomaly detection techniques to locate anomalies within the dataset. The analysis conducted provides valuable insights into the temporal patterns exhibited by significant components of blockchain technology, including Transactions, Blocks, and Output Satoshis. Following this, we proceed to assess the effectiveness of the LSTM and Isolation Forest methods in recognising anomalous instances. The results of this study offer a significant basis for improving the security of blockchain systems by implementing anomaly detection techniques.
Shereen Ismail, Muhammad Nouman, Diana W. Dawoud, Hassan Reza
Cyber-attacks pose a significant challenge to the security of Internet of Things (IoT) sensor networks, necessitating the development of robust countermeasures tailored to their unique characteristics and limitations. Various prevention and detection techniques have been proposed to mitigate these attacks. In this paper, we propose an integrated security framework using Blockchain (BC) and Machine Learning (ML) to protect IoT sensor networks. The framework consists of two modules: a BC prevention module and a ML detection module. The BC prevention module has two lightweight mechanisms: identity management and trust management. Identity management employs a lightweight Smart Contract (SC) to manage the node registration and authentication, ensuring that unauthorized entities are prohibited from engaging in any tasks, while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network's lifetime and tracking historical node behaviors. Consensus and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance (VBFT) mechanism to ensure network reliability and integrity. The ML detection module utilizes LightGBM algorithm to classify malicious nodes and notify the BC network if it must make decisions to mitigate their impacts. We investigate the performance of several off-the-shelf ML algorithms, including Logistic Regression, Complement Naive Bayes, Nearest Centroid, and Stacking, using the WSN-DS dataset. LightGBM is selected following a detailed comparative analysis conducted using accuracy, precision, recall, F1-score, processing time, training time, prediction time, computational complexity, and Matthews Correlation Coefficient (MCC) evaluation metrics.
Cyber-physical systems (CPS) are becoming increasingly ubiquitous, connecting the physical world with the cyber realm. This convergence has exposed CPS to a growing threat landscape, necessitating robust intrusion detection systems (IDS) to safeguard critical infrastructure. Deep learning (DL) has emerged as a powerful tool for IDS, and convolutional neural networks (CNNs) have demonstrated exceptional performance in this domain. However, traditional IDS models are susceptible to data tampering and manipulation, compromising their integrity and effectiveness. Blockchain technology, with its inherent immutability and tamper-proof nature, offers a promising solution to enhance the security and reliability of IDS models. In this study, we propose a CNN-based IDS model that leverages blockchain technology to secure network traffic data. Our hypothesis is that integrating blockchain with CNNs can significantly improve the security and robustness of IDS models against data tampering and manipulation. To test our hypothesis, we employ a greedy-based genetic algorithm to select the most relevant features from network traffic data, followed by training a CNN model using the selected features. Finally, we evaluate the trained CNN model on a real-world dataset, demonstrating its ability to accurately classify network traffic as normal or intrusive. The results of our evaluation reveal that the proposed CNN-based IDS model achieves a classification accuracy of 99.2%, surpassing traditional IDS models. Moreover, our model exhibits enhanced resilience against data tampering and manipulation, demonstrating the effectiveness of blockchain integration in safeguarding the integrity of IDS models. Our findings underscore the potential of blockchain-enhanced CNNs as a robust and secure solution for intrusion detection in CPS, ensuring the integrity and protection of critical infrastructure.
Leandro Cunha, Miguel A. Brito, Domingos F. Oliveira, Ana Paula Martins
The cryptocurrency market has grown significantly, and this quick growth has given rise to scams. It is necessary to put fraud detection mechanisms in place. The challenge of inadequate labeling is addressed in this work, which is a barrier to the training of high-performance supervised classifiers. It aims to lessen the necessity for laborious and time-consuming manual labeling. Some unlabeled data points have labels that are more pertinent and informative for the supervised model to learn from. The viability of utilizing unsupervised anomaly detection algorithms and active learning strategies to build an iterative process of acquiring labeled transactions in a cold start scenario, where there are no initial-labeled transactions, is being investigated. Investigating anomaly detection capabilities for a subset of data that maximizes supervised models’ learning potential is the goal. The anomaly detection algorithms under performed, according to the results. The findings underscore the need that anomaly detection algorithms be reserved for situations involving cold starts. As a result, using active learning techniques would produce better outcomes and supervised machine learning model performance.
The rapid development of blockchain technology and the rise of Ethereum as its representative platform has triggered a wide range of research and applications. However, this development is also accompanied by new security challenges, among which the Eclipse attack is one of the significant security threats currently facing Ethereum networks. In response to these challenges, we propose an Ethereum Eclipse attack detection method based on a multi-head attention mechanism with Bi-LSTM. This approach utilizes the Bi-LSTM model and multi-head attention mechanism to process time-series data, capturing and focusing on the features most relevant to the Eclipse attack for accurate identification. Additionally, we employ PCA and UMAP dimensionality reduction techniques in data preprocessing to enhance processing efficiency. Experimental results demonstrate that this method distinguishes regular traffic from attack traffic more accurately. Compared to the existing random forest method, our detection approach based on a multi-head attention mechanism with Bi-LSTM achieves a higher detection rate and lower false alarm rate, highlighting its effectiveness in addressing Ethereum network security.
The Internet of Things (IoT) is the most abundant technology in the fields of manufacturing, automation, transportation, robotics, and agriculture, utilizing the IoT's sensors-sensing capability. It plays a vital role in digital transformation and smart revolutions in critical infrastructure environments. However, handling heterogeneous data from different IoT devices is challenging from the perspective of security and privacy issues. The attacker targets the sensor communication between two IoT devices to jeopardize the regular operations of IoT-based critical infrastructure. In this paper, we propose an artificial intelligence (AI) and blockchain-driven secure data dissemination architecture to deal with critical infrastructure security and privacy issues. First, we reduced dimensionality using principal component analysis (PCA) and explainable AI (XAI) approaches. Furthermore, we applied different AI classifiers such as random forest (RF), decision tree (DT), support vector machine (SVM), perceptron, and Gaussian Naive Bayes (GaussianNB) that classify the data, i.e., malicious or non-malicious. Furthermore, we employ an interplanetary file system (IPFS)-driven blockchain network that offers security to the non-malicious data. In addition, to strengthen the security of AI classifiers, we analyze data poisoning attacks on the dataset that manipulate sensitive data and mislead the classifier, resulting in inaccurate results from the classifiers. To overcome this issue, we provide an anomaly detection approach that identifies malicious instances and removes the poisoned data from the dataset. The proposed architecture is evaluated using performance evaluation metrics such as accuracy, precision, recall, F1 score, and receiver operating characteristic curve (ROC curve). The findings show that the RF classifier transcends other AI classifiers in terms of accuracy, i.e., 98.46%.
While the popularity of cryptocurrencies and the whole industry's value are rising, the number of threat actors who use illegal “coin miner mal ware” is increasing as well. The threat actors commonly use computational resources of companies, research and educational institutions, or end users. In this paper, we analyzed the long-term periodic behavior of the cryptocurrency miners communicating in computer networks. We propose a novel method for cryptominers detection using specially designed periodicity features. The detection algorithm is based on the mathematical detection of periodic Flow time series (FTS) and feature mining. Altogether with the Machine Learning technique, the resulting system achieves high-precision performance. Furthermore, our approach enhances a flow-based cryptominers detection system DeCrypto to further improve its reliability and feasibility for high-speed networks.
Inspections into cybercrime rely heavily on the use of digital evidence because of its ability to connect individuals to specific illegal activity. During a probe into a computer crime, it is essential that the integrity, authenticity, and auditability of digital evidence be maintained at all times when it is being transferred through the chain of custody from the beginning to the finish. The digitalization of banking is paralleled by an equally digitalization of the environment for financial crime. Because laws, rules, and forensic techniques are unable to keep up with the fast development of new technologies, investigations into embezzlement schemes might benefit from the standardization of processes and recording of the related approach. The applicability and adaptability of our method may be extended to include a wide variety of fraud investigations as well as routine internal audits. We offer a working Ethereum-based solution, and we incorporate standardised forensic processes and chain of custody preservation techniques. In conclusion, we investigate the challenges surrounding the mutually beneficial link between blockchain technology and financial investigations, as well as the managerial effect and potential avenues for further study. r wicked actors. In this sense, the characteristics afforded by blockchain technology, such as immutability, verifiability, and authentication, contribute to an increase in the degree of rigor that may be achieved in financial forensics. In this article, we describe not only the current status of blockchain-based digital forensic procedures but also a taxonomy of the most popular methodologies used in financial investigations. Our solution makes it possible for consumers to trace the history of their data by making use of smart contracts (CS). In conclusion, the development of an Artificial Neural Network (ANN) for blockchain makes the collection of evidence more easier. Java, which is used for clouds and blockchains, and network simulator-3.26, which is used for software-defined networking (SDN), are both used inside a single testing environment. Response time, Evidence input time, Evidence verification time, All aspects of the suggested forensic architecture, including communication overhead, hash calculation time, key generation time, encryption time, decryption time, and overall change rate, show potential.
Federated Learning (FL) systems are susceptible to adversarial attacks, such as model poisoning attacks and backdoor attacks. Existing defense mechanisms face critical limitations in deployments, such as relying on impractical assumptions (e.g., adversaries acknowledging the presence of attacks before attacking) or undermining accuracy in model training, even in benign scenarios. To address these challenges, we propose CustodianFL, a two-staged anomaly detection method specifically designed for FL deployments. In the first stage, it flags suspicious client activities. In the second stage that is activated only when needed, it further examines these candidates using Three-Sigma Rule to identify and exclude truly malicious local models from FL training. To ensure integrity and transparency within the FL system, CustodianFL integrates zero-knowledge proofs, enabling clients to cryptographically verify the server's detection process without relying on the server's goodwill. CustodianFL operates without unrealistic assumptions and avoids interfering with FL training in attack-free scenarios. It bridges the gap between theoretical advances in FL security and the practical demands of real FL systems. Experimental results demonstrate that CustodianFL consistently delivers performance comparable to benign cases, highlighting its effectiveness in identifying and eliminating malicious models with high accuracy.
The recent surge in the attention garnered by blockchain technology, an immutable ledger enabling decentralized transactions, is noteworthy.However, the security of blockchain remains susceptible to various attacks, including distributed denial-of-service (DDoS) attacks, which have increasingly targeted Bitcoin services.In response, deep learning algorithms have emerged as a potent solution to complex problems within the realm of information science.This study proposes a novel approach, utilizing these algorithms within hybrid frameworks, to address intricate cybersecurity issues.The methodologies were implemented and fine-tuned within a Python environment.Initially, a technique known as data augmentation was applied to an experimental domain aimed at verifying efficiency and boosting precision in complex datasets.Data augmentation, a method of generating new data points from existing ones, artificially enhances the volume of data.A Conditional Table Generative Adversarial Network (CTGAN) approach was adopted for the creation of tabular synthetic data.The utilization of synthetic data was found to enhance the model's performance and robustness compared to the exclusive use of original data.Subsequently, a binary classification hybrid deep learning model, incorporating Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) algorithms, was proposed for the detection of DDoS attacks within cryptocurrency networks.The proposed model was then validated using actual instances of DDoS attacks within the Bitcoin service dataset.The validation process incorporated a test set comprising 20% of the augmented data.Evidently, the proposed model outperformed standard deep learning implementations, achieving an impressive accuracy of approximately 95.84%.This study, therefore, presents a promising approach to mitigating DDoS attacks within the Bitcoin ecosystem.
A blockchain is a distributed ledger composed of immutable blocks of data that often refer to money transfers. As blockchain networks gain popularity, there is a rising concern for security against malicious and hacking users. Detection anomalies and unusual account activities can be based on comparing upcoming activity with recent and historical data. However, the size and rapid growth of the complete blockchain history can result in slow and expensive processing. This paper proposes a solution to this challenge by analyzing summarized block data structures, known as sketches, instead of the entire blockchain. Sketches are commonly used in computer systems and blockchain networks to provide efficient query executions while maintaining a compact data representation. This study explores the use of sketches, such as Bloom Filter and HyperLogLog, to identify suspicious accounts without requiring the examination of the entire blockchain data. We design solutions for anomaly detection of certain goals that may be indications of known attacks. We develop methods to identify accounts with high transaction volume, frequency, and node degree. Furthermore, the innovation of this paper lies in the generalization of sketch-based anomaly detection through a generic solution capable of addressing diverse queries. We conduct experiments based on real Ethereum data and compare the accuracy, time complexity, and memory usage of our algorithms with traditional detection algorithms that rely on the complete blockchain data. Our results indicate that sketch-based anomaly detection methods can provide a practical and scalable solution for detecting anomalies in transactions on blockchain networks. We managed to reduce the amount of memory used by the detection process by 90%-96% and reduce the time complexity by 86% while maintaining high accuracy.
Beibei Han, Yingmei Wei, Qingyong Wang, Francesco Maria De Collibus · 5 authors
Abstract In recent years, a surge of criminal activities with cross-cryptocurrency trades have emerged in Ethereum, the second-largest public blockchain platform. Most of the existing anomaly detection methods utilize the traditional machine learning with feature engineering or graph representation learning technique to capture the information in transaction network. However, these methods either ignore the timestamp information and the transaction flow direction information in transaction network or only consider single transaction network, the cross-cryptocurrency trading patterns in Ethereum are usually ignored. In this paper, we introduce a Multi-layer Temporal Transaction Anomaly Detection (MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD) model in Ethereum network with graph neural network. Specifically, for a given Ethereum token transaction network, we first extract its initial features including the structure subgraph and edge’s feature. Then, we model the temporal information in subgraph as a series of network snapshots according to the timestamp on each edge and time window. To capture the cross-cryptocurrency trading patterns, we combine the snapshots from multiple token transactions at a given timestamp, and we consider it as a new combined graph. We further use the graph convolution encoder with attention mechanism and pooling operation on this new graph to obtain the graph-level embedding, and we transform the anomaly detection on dynamic multi-layer Ethereum transaction networks as a graph classification task with these graph-level embeddings. MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD can integrate the transaction structure feature, edge’s feature and cross-cryptocurrency trading patterns into a framework to perform the anomaly detection with graph neural networks. Experiments on three real-world multi-layer transaction networks show that the proposed MT $$^2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mrow/> <mml:mn>2</mml:mn> </mml:msup> </mml:math> AD (0.8789 Precision, 0.9375 Recall, 0.4987 FbMacro and 0.9351 FbWeighted) can achieve the best performance on most evaluation metrics in comparison with some competing approaches, and the effectiveness in consideration of multiple tokens is also demonstrated.
In recent years, the attention drawn by cryptocurrencies has increased as their popularity grows rapidly. This situation attracts investors, entrepreneurs, regulators, and the general public. However, these coins may die and become dead coins. A coin is declared dead if no activity is recorded for more than one year. Numerous coins die without completing their one-year timeframe and this issue causes investors to lose a significant amount of money. In this study, we develop a deep neural network architecture based on long short-term memory (LSTM) to predict the death risk of a coin in a specified timeframe. In order to do this, time-series data consisting of the closing price and volume values of 4733 dead coins are utilized. The goal of our model is to inform investors about the death risk of the coin and improve their overall portfolio performance.
In recent years, blockchain technology has gained widespread attention for its distributed and immutable ledger system that ensures security and transparency. However, the decentralized nature of the blockchain network also presents unique challenges in detecting fraudulent activities, such as money laundering, phishing, and other illicit transactions that may be executed by malicious actors. The traditional detection methods, such as rule-based systems, may not be sufficient to capture the complex and evolving nature of these activities. This paper proposes an anomaly detection approach for significant entity identification within Worldwide Asset Exchange (WAX) blockchain network using unsupervised machine learning. The proposed approach was evaluated by utilizing three detection algorithms (CBLOF, AE, and IF) for assigning an anomaly score to each account in the dataset. The results indicate the presence of potentially fraudulent activities and the effectiveness of the anomaly ranking mechanism in identifying such cases.
Blockchain is a growing technology nowadays. This technology is often associated with the terms "distributed" and "decentralized" technology that does not require a central authority (CA) to control certain processes Therefore, the acceptance of this technology is increasing with the introduction of some of the latest applications in the world of decentralized financial (DeFi), non-fungible tokens (NFT), and metaverse. Previously, blockchain technology was widely adopted in several popular domains, such as digital government [2], health [3], business management [4], and smart cities The understanding of blockchain is linked to several main functions: public key, private key, hash value, and peer-to-peer (P2P) network.
Mohiuddin Ahmed, Clark Pagutaisidro, Apichart Alexander Pike, Yuting Yang · 5 authors
This paper presents a ransomware payment transactions repository, RansomCoin, and showcases the pattern analysis to understand the behaviour of ransomware attackers' money laundering tactics. The surge in ransomware attacks globally is an exciting research challenge and needs a sustainable solution. In this work, we created a bitcoin transaction dataset related to ransomware. In particular, we have focused on Qlocker, Medusa-Locker, BitPaymer, DarkSide, and Netwalker ransomware attacks to create an automation process that extracts data associated with these attacks from the blockchain. The automation details are publicly available via GitHub. The RansomCoin dataset will help law enforcement agencies trace the transaction and analyse the bitcoin movements in the blockchain. The dataset contains suspicious/normal flags, which can help focus on the wallet address flagged as suspicious. The k-NN algorithm from the family of anomaly detection techniques performs better in identifying suspicious transactions.
Abstract With the high-speed development of decentralized applications, account-based blockchain platforms have become a hotbed of various financial scams and hacks due to their anonymity and high financial value. Financial security has become a top priority with the sustainable development of blockchain-based platforms because of an increasing number of cyber attacks, which have resulted in a huge loss of crypto assets in recent years. Therefore, it is imperative to study the real-time detection of cyber attacks to facilitate effective supervision and regulation. To this end, this paper proposes the weighted and extended isolation forest algorithms and designs a novel framework for the real-time detection of cyber-attack transactions by thoroughly studying and summarizing real-world examples. Furthermore, this study develops a new detection approach for locating the compromised address of a cyber attack to resolve the data scarcity of hack addresses and reduce time consumption. Moreover, three experiments are carried out not only to apply on different types of cyber attacks but also to compare the proposed approach with the widely used existing methods. The results demonstrate the high efficiency and generality of the proposed approach. Finally, the lower time consumption and robustness of our method were validated through additional experiments. In conclusion, the proposed blockchain-oriented approach in this study can handle real-time detection of cyber attacks and has significant scope for applications.
R. Saravanan, S. Santhiya, K Shalini, V.S Sreeparvathy
Anomaly detection is one of the challenging problems encountered by the modern network security industry. In these last years, Blockchain technologies have been widely used in several application fields to improve data privacy and trustworthiness and security of systems. Despite being an effective tool, the blockchain is not impervious to cyberattacks. For instance, a successful 51% attack on Ethereum Classic exposed security flaws in the technology. Attacks can be viewed from a statistical standpoint as an aberrant finding that strongly deviates from the norm. Machine learning is a science whose objective is to discover insights, trends, and anomalies in massive data sets; as a result, it can be used to detect blockchain attacks. In this work, we define a federated learning-based anomaly detection system that is trained using aggregate data gathered from observing blockchain activity on the end device itself. Experiments on the whole historical logs of the Ethereum Classic network demonstrate our model’s ability to accurately identify assaults that have been made public while also automatically signing digital transactions for further protection. Therefore, it is necessary to create an anomaly detection system that can monitor networks for any dangerous actions and produce findings for the management authority in the end device itself. Several classification techniques and machine learning algorithms have been taken into consideration in our suggested article to categorize the accurate model.