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.
Security in the blockchain has become a topic of concern because of the recent developments in the field. One of the most common cyberattacks is the so-called phishing attack, wherein the attacker tricks the miner into adding a malicious block to the chain under genuine conditions to avoid detection and potentially destroy the entire blockchain. The current attempts at detection include the consensus protocol; however, it fails when a genuine miner tries to add a new block to the blockchain. Zero-trust policies have started making the rounds in the field as they ensure the complete detection of phishing attempts; however, they are still in the process of deployment, which may take a significant amount of time. A more accurate measure of phishing detection involves machine-learning models that use specific features to automate the entire process of classifying an attempt as either a phishing attempt or a safe attempt. This paper highlights several models that may give safe results and help eradicate blockchain phishing attempts.
In recent years, there has been a significant rise in the popularity of cryptocurrency amongst investors worldwide. One cryptocurrency that has been the forerunner in this new digital age is Satoshi Nakamoto’s Bitcoin. As much as it has augmented in value in the past several years, many issues have emerged as new points of concern surrounding cryptocurrency ransomware orchestrated by scammers. As a result of the growing scandals, one notorious case that has made the most headlines is the Bitcoin Heist. We have found a sizable dataset that traces back to the Bitcoin Heist incident. With the help of data science and machine learning fundamentals, we will explain different methodologies to determine whether transactions are malicious or not based on a given Bitcoin address. In this paper, we will explain cryptocurrency and ransomware and further insights into the machine learning concepts behind this issue through various models such as Adaptive Boosting (AdaBoost), Gradient Boosting, K-Nearest Neighbor (KNN), and Random Forest.
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.
In addition to the physical security of energy networks, cyber security is essential to protecting these systems as well. Cyber threats can stem from malicious hackers who have infiltrated the networks to gain unauthorized access to sensitive data, or from vulnerabilities within the systems themselves. It is increasingly important that smart grid companies invest in cyber security solutions, such as strong passwords, two-factor authentication, encryption, and regular software updates, to counteract these threats. Additionally, it is beneficial for organizations to create incident response plans that are tailored to their specific needs, and which define the chain of command and actions to take in the case of an incident. To details the usage of smart grids in various domains and places in an effective way and explains the efficient way of consuming power for smart ventilators by monitoring and providing cyber security against cyber-attacks. The distributed power from various regions is collected from the less predominant places and supplied to the smart ventilators through smart inverters.
Domain Name System(DNS) is a crucial component for us to access the Internet. However, it is vulnerable to several attacks such as DNS Cache Poisoning Attack and DNS DDoS Attack. Because of the high security promised by the Blockchain technology, we investigate the domain name systems based on blockchain, and focus on Ethereum Name Service(ENS). In this paper, we propose an ENS-oriented light node in order to solve the Full-node problem which means each participant has to hold a large amount data.
Smart contract is the building block of blockchain systems that enables automated peer-to-peer transactions and decentralized services. Smart contracts certainly provide a powerful functional surplus for maintaining the consistency of transactions in applications governed by blockchain technology. Smart contracts have become lucrative and profitable targets for attackers because they can hold a large amount of money. Formal verification and symbolic analysis have been employed to combat these destructive scams by analyzing the codes and function calls, yet each scam's vulnerability should be discreetly predefined. In this work, we introduce ADEFGuard, a new anomaly detection framework based on the behavior of smart contracts, as new features. We design a learning and monitoring module to determine fraudulent smart contract behaviors. Our framework is advantageous over basic algorithms in three aspects. First, ADEFGuard provides a unified solution to different genres of scams, relieving the need for code analysis skills. Second, ADEFGuard's inference is orders of magnitude faster than code analysis. Third, experimental results show that ADEFGuard achieves high accuracy (85%), precision (75%) and recall (90%) for malicious contracts and is potentially useful in detecting new malicious behaviors of smart contracts.
In recent years, with the continuous promotion of blockchain technology, the application of smart contracts has shown an explosive growth trend, and smart contract vulnerabilities seriously threaten the ecological security of blockchain. Aiming at the inefficiency of existing smart contract Symbolic Execution vulnerability detection technology, this paper proposes an effective smart contract vulnerability detection method at the source code level. Firstly, we define the critical path. As attackers typically aim to steal or freeze funds, we define the path related to fund transfer as the critical path, and its related instructions are the critical instructions. Then, we constructed a smart contract control flowchart based on Ethereum bytecode and used a constraint solver to solve path constraints and corresponding vulnerability constraints. Detect common smart contract vulnerabilities such as reentrancy, access control, arithmetic vulnerabilities, unchecked low calls, and denial of service. The experimental results show that the proposed scheme has good detection performance, and vulnerability detection was performed on 55 smart contracts containing vulnerabilities in the dataset. Compared with the pre optimized scheme, the precision rate of this scheme has been improved by 7.52%, and the total execution time has been reduced by 34.92%.
Since the invention and popularisation of blockchain technology, we have seen a recent surge of attacks targeting cryptocurrency infrastructure. Alongside this, botnet malware has become a staple within threat actors’ toolkits, and have often been used to target a wide range of devices. This paper explores the threats that a custom built botnet poses when used to target cryptocurrency mining software. The botnet within this project is developed in the programming language Golang, due to its effective networking and utilisation in the malware development sector. The targets of the attack will be a cryptocurrency miner, and for ethical reasons a proof-of-concept miner will be used for testing. For evaluation purposes a fully virtualised network is used, with practical exploitation taking place to evaluate some metrics of performance for the botnet. With these metrics, the potential threats posed are then explored, with the main attack vector discovered being defined as “forced pooling”. Through this attack vector we show how this unique threat facilitates a variety of different attacks, both on and off chain including a “51% attack” and password cracking; exploring how a potential distributed supercomputer can be used as an attack tool.
To provide customized and high-quality network services under limited network resources, the 5G introduces the network slicing technology that divides physical networks into several logically independent virtual networks, improving the performance of network utilization. The slice management should satisfy the chief concerns of network operators and slice tenants who are the two most important participants, i.e., slice allocation for operators and Service Level Agreement (SLA) guarantee for tenants. However, for slice allocation, traditional centralized schemes cannot well support multi-operator slicing due to the lack of trust. And for SLA guarantee, existing solutions only provide global SLA based on game theory but cannot handle each dispute between operators and tenants. To solve the problems, we propose a blockchain-based network slice management framework consisting of a slice committee and three protocols: slice, audit, and dispute. With the help of the decentralization and reliability of blockchain, the proposed scheme achieves collaborative slice management among multiple operators with SLA guarantee. Through security and performance analysis, we prove that the proposed scheme can defend against possible dishonest behaviors of entities in the system, and is practical in terms of performance.
Because of its ability to completely revamp current blockchain security methods, this connection is crucial. An effective safeguard against complex assaults, Nonlinear Dynamics (ND) adds a living, breathing component to consensus methods and cryptographic primitives. There is an urgent need for creative, nonlinear methods to strengthen blockchain security in light of present challenges including increasing attack vectors and risks posed by quantum computing. The suggested Dynamic Chaos-based Blockchain Security (DC-BS) system in this paper makes use of the chaotic dynamics present in ND to strengthen various aspects of blockchain security. Adaptive threat detection systems, dynamic consensus methods, and chaos-based encryption are all newly introduced in DC-BS. Validation of DC-BS's efficacy in preventing various attack scenarios through simulation studies demonstrates its advantages in reducing vulnerabilities and responding to new attack types. Various decentralized systems can benefit from DC-BS, including as supply chain management, the Internet of Things (IoT), conventional blockchain networks, and decentralized finance (DeFi). To strengthen the security of various decentralized applications, DC-BS works to increase trust, transparency, and resilience. The effectiveness of DCBS is confirmed by thorough simulation analyses that cover a wide range of attack scenarios, including double-spending assaults, Sybil attacks, and eclipse attacks. Based on the results of the simulations, DCBS is much more effective than conventional blockchain security procedures at reducing these risks. Showcased as well is the technique's capacity to react to changing attack techniques, highlighting its capacity to provide strong security even in dynamic settings.
Tao Huang, Renchao Xie, Yuzheng Ren, F. Richard Yu · 10 authors
In recent years, the Industrial Internet and Industry 4.0 came into being. With the development of modern industrial intelligent manufacturing technology, digital twins, Web3 and many other digital entity applications are also proposed. These applications apply architectures such as distributed learning, resource sharing, and arithmetic trading, which make high demands on identity authentication, asset authentication, resource addressing, and service location. Therefore, an efficient, secure, and trustworthy Industrial Internet identity resolution system is needed. However, most of the traditional identity resolution systems follow DNS architecture or tree structure, which has the risk of a single point of failure and DDoS attack. And they cannot guarantee the security and privacy of digital identity, personal assets, and device information. So we consider a decentralized approach for identity management, identity authentication, and asset verification. In this paper, we propose a distributed trusted active identity resolution system based on the inter-planetary file system (IPFS) and non-fungible token (NFT), which can provide distributed identity resolution services. And we have designed the system architecture, identity service process, load balancing strategy and smart contract service. In addition, we use Jmeter to verify the performance of the system, and the results show that the system has good high concurrent performance and robustness.
This paper focuses on achieving high-level security in Mobile Adhoc Networks (MANET) by incorporating Blockchain technology-based Intrusion Detection systems (IDS). The existing works on MANET security focus on either security prevention or detection. Thus, the security level attained by the prior works is unable to cope with the increasing attacks. To resolve this main issue, this research paper introduces Lightweight Blockchain assisted Intrusion Detection System (LB-IDS) which jointly prevents and detects the attacks held on mobile networks. Initially, the network nodes are authenticated by a lightweight Blockchain-based Multi-Factor Authentication (LBMFA) scheme. This procedure prevents the malicious nodes entry to the network. Then, data packets are transmitted through the optimal route which is selected by Multi-Objective Strawberry Optimization (MOSO) algorithm. The collected data packets are fed into IDS which classifies the data into normal and malicious packets. For IDS, we proposed Deep Q-Learning (DQL) algorithm which takes actions by learning the environment. As the mitigation step, the Blockchain is updated with the trust value according to the data packet classification. For such continuous monitoring, K-Mode Clustering (KMC) algorithm is proposed. On the whole, the proposed work improves the network security in MANET through Prevention, Detection, and Mitigation. The results of the presented work attains better security level, packet delivery ratio (PDR), energy efficiency, delay, and detection accuracy.
To address the problems that current studies for enhancing network accountability based on IPv6 addresses do not support cross-Autonomous Systems (AS) or restrict threatener behaviors, a distributed IPv6 Address Traceback and Threatener Restriction Mechanism (ATTRM6) based on smart contract is proposed. Tracing servers of each AS form a blockchain and invoke smart contract functions to store address information of different ASes on the blockchain. When IPv6 address traceback is needed across ASes, the traceback server of a specific AS reads addresses information stored on the blockchain to identify the threatener. Considering the restriction scheme for threatener associated with IPv6 addresses, and proposing a Punishment-Forgiveness Policy (PFP) to dynamically adjust the reputation of threatener, and store restricted threatener and their reputation on the blockchain, thus providing data support to each AS to take restriction measures. Compared with information sharing based on a centralized database, the ATTRM6 mechanism can accomplish more reliable sharing. Experimental results show that the ATTRM6 mechanism has low overhead and can effectively perform IPv6 address traceback and threatener restriction.
Viji Rajendran, A. Jasrotia, Ghulam Murtaza, Rohit Sharma
Any democracy must have an open voting<br> process that satisfies the needs of the populace to give the<br> appropriate individual the power. Additionally, the<br> traditional voting systems currently in use have<br> significant flaws and lack security and transparency. It<br> has long been difficult to create a safe electronic voting<br> system that provides the transparency and flexibility<br> provided by electronic systems, while maintaining the<br> fairness and privacy of present voting schemes. In this<br> project, we assess a blockchain-based implementation of<br> distributed electronic voting systems. It addresses some<br> of the well-known blockchain frameworks with the aim<br> of building a blockchain-based electronic voting system<br> and presents a novel electronic voting system based on<br> blockchain that tackles some of the shortcomings in<br> existing systems. In particular, we evaluate the potential<br> of distributed ledger technologies through the<br> description of a case study; namely, the process of an<br> election, and the implementation of a blockchain-based<br> application, which improves the security and decreases<br> the cost of hosting a nationwide election.
Abstract The increasing prominence of cryptocurrencies has brought to the forefront the critical issue of security vulnerabilities, particularly the majority 51% attack. This study addresses the need for benchmarks to distinguish between vulnerable and non-vulnerable cryptocurrencies. A comprehensive literature review reveals a lack of research with the desired statistical rigor in this domain, necessitating the development of a robust model. Drawing upon mathematical modeling, this research fills the gap by proposing a susceptibility test model which incorporates essential parameters identified from literature. The model is validated with additional data to ensure its accuracy and reliability. Furthermore, k-means clustering analysis is employed to determine benchmarking thresholds, allowing for a refined categorization of cryptocurrencies based on their susceptibility levels. The findings of this study reveal five distinct clusters, each representing a unique security profile. Resilience is associated with susceptibility test values less than the critical threshold \(0.532\). In contrast, cryptocurrencies with susceptibility test values greater than 1.557 exhibit alarming vulnerability. In between, three cryptocurrency susceptibility levels are identified, ranging from moderate resilience to high vulnerability. The outcomes of this study serve as a foundation for better-informed investment decisions as well as future research endeavors, informing the development of industry best practices and policy recommendations aimed at strengthening the robustness of cryptocurrencies against malicious activities.
Abdullah Lakhan, Orawit Thinnukool, Tor Morten Groenli, Pattaraporn Khuwuthyakorn
These days, the use of digital healthcare has been growing in practice. Getting remote healthcare services without going to the hospital for essential checkups and reports is easy. It is a cost-saving and time-saving process. However, digital healthcare systems are suffering from security and cyberattacks in practice. Blockchain technology is a promising technology that can process valid and secure remote healthcare data among different clinics. However, ransomware attacks are still complex holes in blockchain technology and prevent many healthcare data transactions during the process on the network. The study presents the new ransomware blockchain efficient framework (RBEF) for digital networks, which can identify transaction ransomware attacks. The objective is to minimize transaction delays and processing costs during ransomware attack detection and processing. The RBEF is designed based on Kotlin, Android, Java, and socket programming on the remote process call. RBEF integrated the cuckoo sandbox static and dynamic analysis application programming interface (API) to handle compile-time and runtime ransomware attacks in digital healthcare networks. Therefore, code-, data-, and service-level ransomware attacks are to be detected in blockchain technology (RBEF). The simulation results show that the RBEF minimizes transaction delays between 4 and 10 min and processing costs by 10% for healthcare data compared to existing public and ransomware efficient blockchain technologies healthcare systems.
Smart contract vulnerabilities have become a common source of security incidents in the blockchain network in recent years. To mitigate the impact of such vulnerabilities, scholars have been exploring more effective and dependable methods for detecting them. However, existing smart contract vulnerability detection methods suffer from issues like a high rate of false positives and omissions, limited scalability, and reliance on expert knowledge, among others. To address these challenges, this study proposes a hybrid neural network model-based approach to smart contract vulnerability detection that leverages the strengths of different neural networks. By incorporating global context alongside local feature extraction, the method significantly enhances feature extraction rates. Experimental results demonstrate that the proposed method is highly efficient and accurate, making it a suitable solution for detecting smart contract vulnerabilities.
Blockchain has facilitated the growth of cryptocurrencies but has also provided new ideas for illegals to commit fraud. Research on malicious accounts detection shows that the number of malicious accounts is much smaller than that of benign accounts, leading to imbalanced dataset samples. Most researchers adopt the under-sampling method to help deal with this issue, but this method does not correspond to the actual scale. So, we propose an anomaly detection method based on community discovery. Firstly, we use the transaction information in the Ethereum public chain to build a transaction network and use the Louvain algorithm to divide the transaction network into communities. Secondly, we use the LightGBM algorithm to classify the community. Finally, based on the classification results, we use HBOS, LOF, K-Means, KNN and iForest algorithms as benchmark algorithms for anomaly detection and compare the experimental results using the methods in this paper with the results of anomaly detection using the original transaction network. Experimental show that our method can reduce the amount of data by 35.53% and increase the AUC values of the five algorithms by 7.52%, 8.41%, 14.88%, 0.83% and 27.95%.
As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.
Smart contract vulnerabilities have led to significant financial losses, with their increasing complexity rendering outright prevention of hacks increasingly challenging. This trend highlights the crucial need for advanced forensic analysis and real-time intrusion detection, where dynamic analysis plays a key role in dissecting smart contract executions. Therefore, there is a pressing need for a unified and generic representation of smart contract executions, complemented by an efficient methodology that enables the modeling and identification of a broad spectrum of emerging attacks We introduce C lue , a dynamic analysis framework specifically designed for the Ethereum virtual machine. Central to C lue is its ability to capture critical runtime information during contract executions, employing a novel graph-based representation, the Execution Property Graph. A key feature of C lue is its innovative graph traversal technique, which is adept at detecting complex attacks, including (read-only) reentrancy and price manipulation. Evaluation results reveal C lue ’s superior performance with high true positive rates and low false positive rates, outperforming state-of-the-art tools. Furthermore, C lue ’s efficiency positions it as a valuable tool for both forensic analysis and real-time intrusion detection.
Liwa H. Al-Farhani, Yahya Alqahtani, Hamdan Alshehri, John Martin · 6 authors
The widespread use of the Internet of Things (IoT) technology has both good and bad things about it. There must be a full and reliable security system in place for the Internet of Things so that things can work together in a safe way and intrusions cannot happen. There are now many more ways to keep the Internet of Things safe, thanks to a detecting system. As machine learning and deep learning technologies have become better, a lot of good intrusion detection systems have been made. This kind of study is covered. These two types of security are compared in this study. The current machine-based intrusion detection system is broken down into more detailed categories based on detection technology, data source, architecture, and operating method. These categories are as follows: It is talked about how IoT security will grow in the future and how to understand its intrusion detection system too. In this paper, a cloud-based blockchain security model has been presented for secure data transmission over IoT.