As Ethereum emerges as a leading platform in blockchain technology, it increasingly faces security threats, particularly targeting smart contracts and decentralized applications (DApps). Current transaction analysis methods, such as dynamic taint analysis based on transaction tracing, are effective in specific scenarios but often fall short in handling complex logical vulnerabilities. Addressing these challenges, this study introduces a novel method for detecting attack transactions in Ethereum, focusing on the analysis of asset transfer behaviors between transaction initiators and interacting contract accounts. The innovation of this approach lies in its perspective of asset transfer, offering a fresh vantage point for identifying potential attack patterns. In the experimental phase, the method demonstrated effectiveness on existing datasets, albeit with the possibility of false positives in more complex scenarios. Overall, this study provides a new perspective in the field of Ethereum security research and offers valuable insights for enhancing the accuracy and efficiency of blockchain transaction security analysis.
A mempool is a security-critical subsystem in a public blockchain. Recent mempool attacks, notably asymmetric DoS, have shown their ability to severely damage the Ethereum network. This paper tackles the open research problem of designing principled and non-intrusive defenses against asymmetric mempool DoSes with provable security. It presents the first mempool economic-security definitions based on mempool-observable conditions. It then presents SAFERAD, a framework of secure mempool designs with provable security against asymmetric DoSes. To defend against dual attacks by evicting and locking a victim mempool, SAFERAD adopts a non-trivial design of enforcing an upper bound of the attack damage under the locking attacks and a lower bound of the attack cost under the eviction attacks. With a prototype implementation on Geth and evaluation under real transaction traces, the results show SAFERAD has low overhead in latency and block revenue, implying non-intrusiveness and practicality.
Anomaly detection in the financial system has been studied for decades. Anomalies refer to irregular items or events that are different from the majority. Therefore, illegal activities are anomalous by nature because it is opposed to the norm. Preventive actions like anomalous detection play an important role in avoiding incidents that damage people's property. In this paper, the Bitcoin network is the subject of the study. We consider the effectiveness of two unsupervised learning algorithms, Histogram-based Outlier Score (HBOS) and Isolation Forest, for detecting anomalous transactions and wallet addresses. Providing insights into the strengths and weaknesses of HBOS and Isolation Forest for anomaly detection. We also analyze which features are the most important for each algorithm in identifying anomalies. The result shows similar detection for both algorithms. While HBOS has higher wallet visualization score at 0.423, Isolation Forest yields better scores on transaction visualization, dual, and known-thieves evaluations with score of 0.713, 0.681, and 0.035, respectively.
Decentralized applications (Dapps), based on smart contract technology, have been increasingly applied in various fields such as healthcare, industrial IoT, agriculture, financial services, supply chain management, and insurance. In certain complex business scenarios, blockchain may require machine learning models to assist contract business. However, on-chain computations are often costly and slow, and there are limitations on contract size. Due to the transparency of on-chain data, there are also privacy concerns regarding user data during model training and inference. To address these challenges, we propose a trusted off-chain machine learning solution that integrates ZK-SNARK and Oracle technologies. Following the principle of “off-chain computation, on-chain verification”,our approach leverages ZK-SNARK to delegate the computation tasks of machine learning models to a trusted environment under the Oracle off-chain server. This solution significantly reduces the computational costs of the blockchain. User data and models are executed off-chain, effectively safeguarding user privacy. The execution results generate zero-knowledge proofs returned for on-chain verification. We have implemented TOMLS-ZKSO and conducted relevant experiments on insurance contract business on the Chainmaker. Experimental results demonstrate the effectiveness of our approach, with model proof generation taking approximately 0.4 seconds and Dapp response time around 0.52 seconds.
S. Vijayalakshmi, Rohan Yellamilli, R Shobikaa, V Subasree · 6 authors
Network Intrusion Detection Systems are critical in bolstering an organization's security infrastructure. The goal of our paper is to design and build a specialized intrusion detection system for simulated military scenarios. The solution classifies network accesses using machine learning techniques and seamlessly integrates the Ethereum blockchain for secure anomalous access record-keeping and auditing. The machine learning component classifies the records as normal or anomalous using the ensemble learning technique, which is the random forest classifier. Feature selection techniques like Variance Threshold, Spearman's correlation coefficient, Pearson's correlation coefficient, Mutual Information and Chi square tests were used to improve the accuracy of the model and reduce dimensionality. The final accuracy of the model was brought to 96.69%. It then sends the records to the blockchain environment for storage. To ensure data integrity and transparency, anomalous access events will be time-stamped, hashed using IPFS, and published to the blockchain. By providing a hash value and a unique id as input, the user will be able to add new anomalous entries. Furthermore, the user will be able to examine the hashed anomalous data for each csv file using the proper id.
Establishing strong security protocols is crucial in the quickly changing internet of things (IoT) environment to reduce potential risks and weaknesses. It is possible to manage security risks more effectively, but there are also challenges because of the interconnected nature of IoT devices, the introduction of 6G networks, and the incorporation of distributed ledger technology (DLT). The focus of this note is on proactive methods of protecting infrastructure and sensitive data. It explores different management strategies that are intended to mitigate threats in IoT environments. Using a security-by-design methodology is a fundamental tactic for threat mitigation in internet of things settings. Every phase of the lifecycle of an IoT device, from design and development to deployment and operation, must incorporate security measures.
Block chain generation, acknowledged for its decentralized and immutable nature, has emerged as an ability solution to cozy networks from cyber-attacks. The convergence of block chain and community safety has opened up a new paradigm inside the discipline of cyber security. This generation can decorate network safety with the aid of imparting a decentralized and incredibly cozy network infrastructure. One of the key possibilities of this convergence is the usage of block chain’s distributed ledger machine to create a tamper-proof and obvious repository of community sports and transactions. This can help in figuring out and preventing capability cyber-attacks, mitigating community vulnerabilities, and ensuring a comfy statistics switch. Another gain of the block chain era is its capacity to offer tamper-proof identity management, ensuring secure admission to networks and preventing unauthorized rights of entry. It could additionally be used to cozy community gadgets and structures, developing comfy and trustless surroundings for conversation.
Jesús Rosa-Bilbao, Juan Boubeta-Puig, Jesús Lagares-Galán, Mark Vella
Blockchain is a relatively recent technology that provides immutability, traceability and transparency of information, thus building trust in the digital society. Blockchain networks generate a large amount of logs which capture and describe data flowing through the network in the form of transactions, blocks and events. Monitoring these blockchain data from the off-chain world is needed to detect anomalies with the aim of mitigating the risks that may arise as a result of using blockchain technology. However, the real-time monitoring of these logs by off-chain systems has become a challenge from the beginning of 2018 when the blockchain networks reached a high number of daily transactions. In this paper, we propose a portable, maintainable and easily configurable architecture integrating blockchain and complex event processing technologies that allows for both the real-time monitoring of logs generated in Ethereum Virtual Machine (EVM)-compatible blockchain networks and the automatic detection of anomalies in these networks by matching event patterns. This architecture was tested by using vast amounts of blockchain data already publicly registered in Ethereum and Polygon networks. The results demonstrate that the proposed architecture is able to automatically detect anomalies which occur in different blockchain networks, making analytics of blockchain data possible by off-chain systems.
H S Byun, Jueun Kim, Yun-Seok Jeong, Byoungjin Seok · 6 authors
Currently, the monetary value of cryptocurrencies is extremely high, leading to frequent theft attempts. Cyberattacks targeting cryptocurrency wallets and the scale of these attacks are also increasing annually. However, many studies focus on large-scale exchanges, leading to a lack of research on cryptocurrency wallet security. Nevertheless, the threat to individual wallets is real and can lead to severe consequences for individuals. In this paper, we analyze the security of the open-source cryptocurrency wallets Sparrow, Etherwall, and Bither against brute-force attacks, a fundamental threat in password-based systems. As cryptocurrency wallets use passwords to manage users’ private keys, we analyzed the private key management mechanism and implemented a password verification oracle. We used this oracle for brute-force attacks. We identified the private key management mechanism by conducting a code-level investigation and evaluated the three wallets’ security through practical experimentation. The experiment results revealed that the wallets’ security, which depends on passwords, could be diminished due to the password input space and the configuration of password length settings. We propose a general methodology for analyzing the security of desktop cryptocurrency wallets against brute-force attacks and provide practical guidelines for designing secure wallets. By using the analysis methods suggested in this paper, one can evaluate the security of wallets.
Chuyi Yan, Xueying Han, Yan Zhu, Dan Du · 6 authors
Abstract Despite the growing attention on blockchain, phishing activities have surged, particularly on newly established chains. Acknowledging the challenge of limited intelligence in the early stages of new chains, we propose ADA-Spear-an automatic phishing detection model utilizing a dversarial d omain a daptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection. The model effectively identifies phishing behavior in new chains with limited reliable labels, addressing challenges such as significant distribution drift, low attribute overlap, and limited inter-chain connections. Our approach includes a subgraph construction strategy to align heterogeneous chains, a layered deep learning encoder capturing both temporal and spatial information, and integrated adversarial domain adaptive learning in end-to-end model training. Validation in Ethereum, Bitcoin, and EOSIO environments demonstrates ADA-Spear’s effectiveness, achieving an average F1 score of 77.41 on new chains after knowledge transfer, surpassing existing detection methods.
Detecting phishing scams has become an important task in blockchain-based cryptocurrency applications. While many network representation learning-based approaches have been proposed for this task, they suffer from various issues including (1) the requirement of handcrafted features, which may not capture complex relationships and patterns in graph data, and/or (2) considering only node features while ignoring the more significant edge features, and/or (3) incapability of preserving complete network topology, which affects the generalization ability. In this paper, we propose a novel Edge-feature modeling-based Topological Graph Neural Network (ETGNN) to detect phishing scams on Ethereum, which avoids all aforementioned issues of existing approaches. Specifically, ETGNN involves two key components, one responsible for learning weighted features of nodes and edges in the Ethereum transaction graph, and the other responsible for incorporating global topological information of the graph using persistent homology. Finally, phishing scams are detected based on these two learned features. The experimental results demonstrate that ETGNN outperforms the state-of-the-art method with an improvement rate of 14.38% on F1-score.
Federated Learning (FL) was first introduced as an idea by Google in 2016, in which multiple devices jointly train a machine learning model without sharing their data under the supervision of a central server. This offers big opportunities in critical areas like healthcare, industry, and finance, where sharing information with other organizations’ devices is completely prohibited. The combination of Federated Learning with Blockchain technology has led to the so-called Blockchain Federated learning (B.F.L.) which operates in a distributed manner and offers enhanced trust, improved security and privacy, improved traceability and immutability and at the same time enables dataset monetization through tokenization. Unfortunately, vulnerabilities of the blockchain-based solutions have been identified while the implementation of blockchain introduces significant energy consumption issues. There are many solutions that also offer personalized ideas and uses. In the field of security, solutions such as security against model-poisoning backdoor assaults with poles and modified algorithms are proposed. Defense systems that identify hostile devices, Against Phishing and other social engineering attack mechanisms that could threaten current security systems after careful comparison of mutual systems. In a federated learning system built on blockchain, the design of reward mechanisms plays a crucial role in incentivizing active participation. We can use tokens for rewards or other cryptocurrency methods for rewards to a federated learning system. Smart Contracts combined with proof of stake with performance-based rewards or (and) value of data contribution. Some of them use games or game theory-inspired mechanisms with unlimited uses even in other applications like games. All of the above is useless if the energy consumption exceeds the cost of implementing a system. Thus, all of the above is combined with algorithms that make simple or more complex hardware and software adjustments. Heterogeneous data fusion methods, energy consumption models, bandwidth, and controls transmission power try to solve the optimization problems to reduce energy consumption, including communication and compute energy. New technologies such as quantum computing with its advantages such as speed and the ability to solve problems that classical computers cannot solve, their multidimensional nature, analyze large data sets more efficiently than classical artificial intelligence counterparts and the later maturity of a technology that is now expensive will provide solutions in areas such as cryptography, security and why not in energy autonomy. The human brain and an emerging technology can provide solutions to all of the above solutions due to the brain's decentralized nature, built-in reward mechanism, negligible energy use, and really high processing power In this paper we attempt to survey the currently identified threats, attacks and defenses, the rewards and the energy efficiency issues of BFL in order to guide the researchers and the designers of FL based solution to adopt the most appropriate of each application approach.
S. B. Goyal, Anand Singh Rajawat, Ritu Shandilya, Varun Malik
Industrial Internet of Things (IIoT) solutions have transformed industrial productivity and operations. The incorporation of Ethereum blockchain technology into IIoT creates new weaknesses, exposing industrial systems to several cyberattacks. An unique IIoT framework mitigates Ethereum-based attacks in industrial applications to solve these vulnerabilities. This system uses supervised learning and quantum classifiers to detect and fix fraudulent Ethereum transaction patterns in real time. Our methodology has lower false positive rates and higher detection accuracy than conventional methods, according to first trials. This study shows that quantum computing and machine learning (ML) can improve the security of Ethereum-enabled IIoT devices in industry.
A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics.
With improved applications in a variety of industries, the Internet of Things (IoT) and distributed ledger innovation are captivating services and industrial advancements. The IoT and blockchain industries are expected to grow by a factor of three by 2025. As computational power increases, many IoT applications confront substantial challenges with regard to data security and scalability. Additionally, Proof-of-work suffers from a number of problems that make it impractical for IoT applications, therefore Practical Byzantine Fault Tolerance (PBFT) is a widely utilized type of decentralized agreement. Nevertheless, the network node's trust in PBFT is not assured, and the approach to achieving consensus consumes a substantial quantity of network resources. This research proposes a new consensus mechanism known as a Hybrid consensus blockchain algorithm. This includes the features that provide safety into the clustering algorithm for identifying and classifying attackers. To promote cluster diversity in wireless ad hoc networks, a technique called Hybrid fuzzy Possibility C Means Clustering (HFPCM) is developed. For detecting and organizing the intruders in order to create the organization more attack resilient, a Byzantine agreement based cooperative methodology is used.
The inherent anonymity of blockchain technology has made the cryptocurrency sector a breeding ground for a multitude of illicit financial crimes. In the realm of blockchain transaction security, phishing scams are widely considered a highly severe form of deceit, leading to significant economic losses. This paper provides an overview of past research findings on methods for detecting phishing scam in blockchain networks. Extending graph neural network approaches to detect phishing scam in blockchain networks will be of utmost importance. Graph neural networks yield models with superior generalization capabilities in comparison to conventional approaches. Future research will involve examining high-quality datasets and evaluating the influence of transaction graphs and transaction subgraphs on the effectiveness of detecting phishing scam nodes in categorization. The primary objective is to construct a more resilient model that can achieve the desired detection outcomes and differentiate phishing scams nodes with greater efficiency and precision.
Against the backdrop of rapidly advancing cloud storage technology, as well as 5G and 6G communication technologies, group key management faces increasingly daunting challenges. Traditional key management encounters difficulties in key distribution, security threats, management complexity, and issues of trustworthiness. Particularly in scenarios with a large number of members or frequent member turnover within groups, this may lead to security vulnerabilities such as permission confusion, exacerbating the security risks and management complexity faced by the system. To address these issues, this paper utilizes blockchain technology to achieve distributed storage and management of group keys. This solution combines key management with the distributed characteristics of blockchain, enhancing scalability, and enabling tracking of malicious members. Simultaneously, by integrating intelligent authentication mechanisms and lightweight data update mechanisms, it effectively enhances the security, trustworthiness, and scalability of the key management system. This provides important technical support for constructing a more secure and reliable network environment.
Ethereum is the second-largest blockchain platform, and the financial value of its cryptocurrency has constantly increased. Unfortunately, regulatory challenges have resulted in a surge of scams, particularly phishing, which now accounts for over 50% of fraudulent funds. Therefore, phishing scam issues have become a top priority, thus calling for dynamic early warning and accurate identification to achieve effective market regulation. However, the existing works focusing on phishing address detection do not consider early warnings for phishing scams. Furthermore, these methods depend on static graphs to extract node information and overlook the dynamic evolution process of the Ethereum network. In this article, we propose EWDPS, a novel framework to achieve dynamic early warning and effectively identify phishing scams on Ethereum. Specifically, we create a new network called the dynamic temporal transaction network (DTTN), which effectively models the dynamic temporal evolution of transactions. In DTTN, we propose the concepts of temporal evolution interaction network and account feature interaction network. Next, we design a novel feature extraction module to capture temporal sequential patterns effectively. This module takes full advantage of the dynamic interaction process of node-related transactions. Finally, we innovatively use the extracted account, network, and temporal features to enhance transaction representation in multiple dimensions. Extensive experiments show that our proposed scheme effectively achieves dynamic early warning and accurately identifies phishing scams. EWDPS achieves 92.20% accuracy, 95.90% precision, 96.77% recall, and 96.53% F1-score, and outperforms the state-of-the-art methods in phishing address identification.