Abstract For a long time, anomaly detection is such a well topic. Its use in the banking industry has aided in the detection of questionable hacking activity. In the network of bitcoin, since all nodes are unlabeled, there is no proof that any particular transaction is the result of illegal activity, this thesis seeks to identify transactions that are unusual or suspicious. Finding abnormalities in the bitcoin transaction network is the main objective. We discuss anomaly identification in this paper with particular reference to the Bitcoin transaction network(BTN). In this instance, anomalies behaviors is a proxy for apprehensive activity, thus our objective is to find anomalies in the dataset in terms of their percentage. To achieve this, we use the feature selection method which is sequential forward feature selection along with three ML techniques, k-means clustering, isolation forest, and support vector machine (SVM) and got the highest accuracy of 98.2% in SVM as compared to all other methods.
A smart contract, as an important part of blockchain technology, has attracted considerable interest from both industry and academia. It provides the basis for the realization of a variety of practical blockchain applications and plays a crucial role in the blockchain ecosystem. While it also holds a large number of digital assets, the frequent occurrence of smart contract vulnerabilities have caused huge economic losses and destroyed the blockchain-based credit system. Currently, the security and reliability of smart contracts have become a new focus of research, and there are a number of smart contract vulnerability detection methods, such as traditional detection tools based on static or dynamic analysis. However, most of them often rely on expert rules, and therefore have poor scalability and high false negative and false positive rates. Recent deep learning methods alleviate this issue, but without considering the semantic information and context of source code. To this end, we propose a hybrid attention mechanism (HAM) model to detect security vulnerabilities in smart contracts. We extract code fragments from the source code, which focus on key points of vulnerability. We conduct extensive experiments on two public smart contract datasets (a total of 24,957 contracts). Empirical results show remarkable accuracy improvement over the state-of-the art methods on five kinds of vulnerabilities, where the detection accuracy could achieve 93.36%, 80.85%, 82.56%, 85.62%, and 82.19% for reentrancy, arithmetic vulnerability, unchecked return value, timestamp dependency, and tx.origin, respectively.
Md. Tauseef, Manjunath R Kounte, Abdul Haq Nalband, Mohammed Riyaz Ahmed
The emergence of the Internet of Things (IoT) has revolutionized the way we interact with the physical world. The rapid growth of IoT devices has led to a pressing need for robust security measures. Two promising approaches that can enhance IoT security are blockchain and artificial intelligence (AI). Blockchain can offer a decentralized and tamper-proof framework, ensuring the confidentiality and integrity of IoT data. AI can analyze large volumes of real-time data and detect anomalies in response to security threats in the IoT ecosystem. This paper explores the potential of these technologies and how they complement each other to provide a secured IoT system. Our main argument is that combining blockchain with AI can provide a robust solution for securing IoT networks and safeguarding the privacy of IoT users. This survey paper aims to provide a comprehensive understanding of the potential of these technologies for securing IoT networks and discuss the challenges and opportunities associated with their integration. It also provides a discussion on the current state of research on this topic and presents future research directions in this area.
Accelerated technological development has brought many novelties, among which is distributed ledger technology (DLT), often called blockchain (BC). BC is perceived as a peer-to-peer distributed immutable ledger that could revolutionize economies, societies and even our daily lives. All protocols for dealing with data and transactions are coded with an algorithm, so there is no need to trust the other contracting party or the intermediary. With the concept of decentralization and the absence of hierarchy, BC wants to avoid all traditional intermediaries and any regulation. The question arises, are BCtechnologies really decentralized and who controls them? What are the consequences if decision-making in BC is influenced by small groups of people or corporations? This article, in an attempt to answer these questions, explores technological scandals in which there have been significant deviations from the basic principles of BC (The DAO Hack, Parity's Smart Contract Bug on Ethereum and Facebook's Libra). Analysis of the above scandals suggests that decentralization is threatened and the current regulatory status of BC is substandard. It was shown that BC technology, due to its deterministic nature, cannot provide solutions for all life situations and that human judgment is irreplaceable.
This article discusses the growing concerns regarding the convergence of virtual currencies and mainstream finance, which is leading to an increase in illicit activities such as money laundering and terrorism financing. The challenges that law enforcement faces in addressing these crimes are exacerbated by limited technological expertise and a sense of impunity among perpetrators. The article highlights successful asset recovery cases involving crypto assets in the United States and the extension of anti-money laundering laws to virtual assets in the United Kingdom and India. While advanced jurisdictions are making progress in addressing these challenges, the article emphasizes the need for policy recommendations and best practices, particularly for jurisdictions in Africa, which is experiencing rapid growth in the crypto market. It also delves into potential avenues for collaboration between the European Union (EU) and India in addressing capacity deficiencies in developing or least developed countries. The cybersecurity practices and frameworks employed by both European and Indian entities may serve as instructive models for developing and least developed countries to combat terrorism financing with virtual assets.
Muhammad Saad, Maaz Bin Ahmad, Muhammad Asif, Muhammad Khalid Khan · 7 authors
Pandemics have always been a nightmare for humanity, especially in developing countries. Forced lockdowns are considered one of the effective ways to deal with spreading such pandemics. Still, developing countries cannot afford such solutions because these may severely damage the country’s economy. Therefore, this study presents the proactive technological mechanisms for business organizations to run their standard business processes during pandemic-like situations smoothly. The novelty of this study is to provide a state-of-the-art solution to prevent pandemics using industrial internet of things (IIoT) and blockchain-enabled technologies. Compared to existing studies, the immutable and tamper-proof contact tracing and quarantine management solution is proposed. The use of advanced technologies and information security is a critical area for practitioners in the internet of things (IoT) and corresponding solutions. Therefore, this study also emphasizes information security, end-to-end solution, and experimental results. Firstly, a wearable wristband is proposed, incorporating 4G-enabled ultra-wideband (UWB) technology for smart contact tracing mechanisms in industries to comply with standard operating procedures outlined by the world health organization (WHO). Secondly, distributed ledger technology (DLT) omits the centralized dependency for transmitting contact tracing data. Thirdly, a privacy-preserving tracing mechanism is discussed using a public/private key cryptography-based authentication mechanism. Lastly, based on geofencing techniques, blockchain-enabled machine-to-machine (M2M) technology is proposed for quarantine management. The step-by-step methodology and test results are proposed to ensure contact tracing and quarantine management. Unlike existing research studies, the security aspect is also considered in the realm of blockchain. The practical implementation of the proposed solution also obtains the results. The results indicate the successful implementation of blockchain-enabled contact tracing and isolation management using IoT and geo-fencing techniques, which could help battle pandemic situations. Researchers can also consider the 5G-enabled narrowband internet of things (NB-IoT) technologies to implement contact tracing solutions.
A secured platform is a critical component of digital governance, as it helps to ensure the privacy, security, and reliability of the electronic platforms and systems used to manage and deliver public services. Interoperability and data exchange are essential for digital governance, as they enable different government agencies and departments to share data, information, and resources seamlessly, regardless of the platforms and technologies they use. In this paper, we build a secure platform to enhance the trustworthiness of digital governance interoperability and data exchange using blockchain and deep learning-based frameworks. Initially, an optimal blockchain leveraging approach is designed using the bonobo optimization algorithm to authenticate data generated from smart city environments. Furthermore, we introduce the integration of a lightweight Feistel structure with optimal operations to enhance privacy preservation. This integration provides two levels of security and ensures interoperability and double-secured data exchange in digital governance systems. In addition, we utilize a deep reinforcement learning (DRL) model to detect and prevent intrusions such as fraud/corruption in the smart city data. This approach enhances transparency and accountability in accessing the data and shows its predominance over other cutting-edge techniques on two benchmark datasets, BoT-IoT and ToN-IoT. Furthermore, the effectiveness of the framework in real-time scenarios has been demonstrated through two case studies. Overall, our proposed framework provides a trustworthy platform for digital governance, interoperability, and data exchange, addressing the challenges of privacy, security, and reliability in managing and delivering public services.
Blockchain technology has gained significant attention as a secure and decentralized platform for various applications. However, the immutable and distributed nature of blockchain also presents unique challenges for detecting anomalies and suspicious activities within the network. This research paper proposes a novel approach to anomaly detection in blockchain using machine learning techniques. The goal of this study is to develop an effective and scalable anomaly detection framework that can analyze the vast amount of data generated within a blockchain network and identify irregularities or potential security threats. The proposed framework leverages the power of machine learning algorithms to learn patterns, relationships, and behaviours from historical blockchain data, enabling the detection of anomalous activities in real time.The research paper first focuses on feature extraction techniques tailored specifically for blockchain data. These techniques consider key characteristics of blockchain transactions, such as transaction size, timestamp, and involved addresses, to construct meaningful features that capture the underlying patterns and trends. Various dimensionality reduction techniques are also explored to handle the high-dimensional nature of blockchain data.Subsequently, several machine learning algorithms, including clustering, classification, and anomaly detection methods, are employed to train models using the extracted features. The performance of different algorithms is evaluated using benchmark datasets and real-world blockchain data to assess their accuracy, precision, and recall in detecting anomalies. Additionally, the scalability of the proposed framework is investigated to ensure its effectiveness in large-scale blockchain networks.Furthermore, the research paper investigates the integration of domain-specific knowledge, such as known attack patterns and regulatory compliance rules, into the anomaly detection framework. This hybrid approach combines the strengths of machine learning algorithms with expert knowledge to enhance the accuracy and interpretability of anomaly detection results.The experimental results demonstrate that the proposed anomaly detection framework achieves promising performance in identifying various types of anomalies in blockchain data. It exhibits high detection rates while minimizing false positives, thereby providing valuable insights for blockchain network administrators and regulators to mitigate security risks and safeguard the integrity of blockchain systems. In conclusion, this research paper presents an innovative approach to anomaly detection in blockchain using machine learning. The proposed framework addresses the unique challenges posed by blockchain's decentralized and immutable nature, offering an effective solution for detecting suspicious activities and ensuring the security of blockchain networks. The findings of this study contribute to the growing field of blockchain analytics and have significant implications for real-world blockchain applications in domains such as finance, supply chain management, and healthcare.
The primary objective of this research is to study the effectiveness of blockchain technology in preventing financial cybercrime. In this research, the researcher intends to know the effectiveness of blockchain technology in preventing financial cybercrime, the white-collar crime which is growing drastically globally. The researcher uses the primary method to collect the data. In this study, four different variables that influence financial cybercrime significantly are immutability, smart contract, distributed ledger technology and consensus algorithm. The data was collected from the targeted respondents which are accountants, IT experts and human resources. Statistical Package of the Social Sciences (SPSS) is being utilized to evaluate the relationship between the four variables which able to influence financial cybercrime. A total of 70 survey questionnaires were delivered to the targeted respondents via a convenience sampling strategy. Responses from 70 participants were entered into SPSS one by one to generate descriptive and inferential statistics. Financial cybercrime is positively correlated with immutability, smart contract, distributed ledger technology and consensus algorithms. As a result, the analysis of the collected data for this research rejects the null hypothesis, while supporting the alternative hypothesis. The conclusion that can derive from this research is that users and organizations should be aware of the financial cybercrime risks that take place around them and the importance to have vital tools that are not vulnerable to malicious attacks. This research creates awareness for users and companies on the usage of blockchain to prevent financial cybercrime.
Urvashi Kishnani, Srinidhi Madabhushi, Sanchari Das
Blockchain's influence extends beyond finance, impacting diverse sectors such as real estate, oil and gas, and education. This extensive reach stems from blockchain's intrinsic ability to reliably manage digital transactions and supply chains. Within the oil and gas sector, the merger of blockchain with supply chain management and data handling is a notable trend. The supply chain encompasses several operations: extraction, transportation, trading, and distribution of resources. Unfortunately, the current supply chain structure misses critical features such as transparency, traceability, flexible trading, and secure data storage - all of which blockchain can provide. Nevertheless, it is essential to investigate blockchain's security and privacy in the oil and gas industry. Such scrutiny enables the smooth, secure, and usable execution of transactions. For this purpose, we reviewed 124 peer-reviewed academic publications, conducting an in-depth analysis of 21 among them. We classified the articles by their relevance to various phases of the supply chain flow: upstream, midstream, downstream, and data management. Despite blockchain's potential to address existing security and privacy voids in the supply chain, there is a significant lack of practical implementation of blockchain integration in oil and gas operations. This deficiency substantially challenges the transition from conventional methods to a blockchain-centric approach.
Jan 1, 2023·2nd International Workshop on Decentralized Governance Design at the 35th International Conference on Advanced Information Systems Engineering 2023
In this article, we develop an interdisciplinary analysis of MEV which desires to merge the gap that exists between technical and legal research supporting policymakers in their regulatory decisions concerning blockchains, DeFi and associated risks. Consequently, this article is intended for both technical and legal audiences, and while we abstain from a detailed legal analysis, we aim to open a policy discussion regarding decentralized governance design at the block building layer as the place where MEV occurs. Maximal Extractable Value or MEV has been one of the major concerns in blockchain designs as it creates a centralizing force which ultimately affects user transactions. In this article, we dive into the technicality behind MEV, where we explain the concept behind the novel Proposal Builder Separation design as an effort by Flashbots to increase decentralization through modularity. We underline potential vulnerability factors under the PBS design, which open space for MEV extracting adversarial strategies by inside participants. We discuss the shift of trust from validators to builders in PoS blockchains such as Ethereum, acknowledging the impact that the later ones may have on users' transactions (in terms of front running) and censorship resistance (in terms of transaction inclusion). We recognize that under PBS, centralized (dominant) entities such as builders could potentially harm users by extracting MEV via front running strategies. Finally, we suggest adequate design and policy measures which could potentially mitigate these negative effects while protecting blockchain users.
Smart contracts have led to more efficient development in finance and healthcare, but vulnerabilities in contracts pose high risks to their future applications. The current vulnerability detection methods for contracts are either based on fixed expert rules, which are inefficient, or rely on simplistic deep learning techniques that do not fully leverage contract semantic information. Therefore, there is ample room for improvement in terms of detection precision. To solve these problems, this paper proposes a vulnerability detector based on deep learning techniques, graph representation, and Transformer, called GRATDet . The method first performs swapping, insertion, and symbolization operations for contract functions, increasing the amount of small sample data. Each line of code is then treated as a basic semantic element, and information such as control and data relationships is extracted to construct a new representation in the form of a Line Graph (LG), which shows more structural features that differ from the serialized presentation of the contract. Finally, the node information and edge information of the graph are jointly learned using an improved Transformer–GP model to extract information globally and locally, and the fused features are used for vulnerability detection. The effectiveness of the method in reentrancy vulnerability detection is verified in experiments, where the F1 score reaches 95.16%, exceeding state-of-the-art methods.
Abstract In recent years, smart contract technology has garnered significant attention due to its ability to address trust issues that traditional technologies have long struggled with. However, like any evolving technology, smart contracts are not immune to vulnerabilities, and some remain underexplored, often eluding detection by existing vulnerability assessment tools. In this article, we have performed a systematic literature review of all the scientific research and papers conducted between 2016 and 2021. The main objective of this work is to identify what vulnerabilities and smart contract technologies have not been well studied. In addition, we list all the datasets used by previous researchers that can help researchers in building more efficient machine-learning models in the future. In addition, comparisons are drawn among the smart contract analysis tools by considering various features. Finally, various future directions are also discussed in the field of smart contracts that can help researchers to set the direction for future research in this domain.
Cryptocurrencies are considered relevant assets and they are currently used as an investment or to carry out transactions. However, specific characteristics commonly associated with the cryptocurrencies such as irreversibility, immutability, decentralized architecture, absence of control authority, mobility, and pseudo-anonymity make them appealing for money laundering activities. Thus, the collection and characterization of current cryptocurrency-based methods used for money laundering are paramount to understanding the circulation flows of physical and digital money and preventing this illegal activity. In this paper, a collection of cryptocurrency transaction methods is presented and distributed through the money laundering life cycle. Each method is analyzed and classified according to the phase of money laundering it corresponds to. The result of this article may in the future help design efficient strategies to prevent illegal money laundering activities.
Rogério Tavares, João Paulo Sousa, Bruno Maganinho, João Pedro Gomes
The use of non-fungible tokens (NFTs) in AAA games is a very controversial topic, which leads to negative reactions from the gamer community. The objective of this article is to relate some of these cases that presented visibility in the press and to analyze the reactions this theme generates. To achieve this, we present some cases that had more relevance in the specialized press and, in the sequence, we present a discussion about the main problems pointed out, such as the state of the art of blockchains, energy efficiency, frauds, and currency evasions. Finally, we present some hypotheses to glimpse how NFTs, and their use in games, may happen in the near future.
Smart contracts based on blockchain are widely used in finance, management, Internet of Things, healthcare, and other fields. However, with the rapid development of smart contracts, the corresponding security vulnerability attack cases occur frequently. Existing Ethereum smart contract vulnerability detection tools based on static analysis techniques rely too much on expert rules, for this reason, this paper proposes an Ethereum smart contract vulnerability detection method SCSVM based on support vector machine technology. A representation of smart contracts is constructed based on the word-to-vector technique, the features of Ethereum smart contracts are extracted based on the support vector machine technique, and these features are combined to identify vulnerabilities. Experiments on Smartbugs and Smartbugs-wild show that SCSVM is significantly effective. It achieves a detection accuracy of 87.51%, outperforming five typical static analysis vulnerability detection tools in terms of F1-score. To alleviate the problems of deep learning methods over-relying on large-scale data to train models and collecting a large number of smart contract attack samples in a short period, this paper proposes a basic learner-meta-learner framework, SCLMF. solc-based acquisition of the bytecode of Ethereum smart contract Solidity, on which smart contract representations are constructed via Python and the use of SCLMF for vulnerability detection. The experiments on WScrawlD show that SCLMF has a certain detection effect. Also, to further verify the effectiveness of SCLMF, experiments were conducted on Omniglot, and the detection accuracy was 96.7% and 98.5% under 5-way 1-shot and 5-way 5-shot conditions, respectively, which exceeded Memory-Augmented Neural Networks and CONVOLUTIONAL SIAMESE NETS. In summary, the experiments proved the effectiveness of SCSVM and SCLMF in Ethereum smart contract vulnerability detection.
Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learning-based me... | Find, read and cite all the research you need on Tech Science Press
Lin William Cong, Kimberly Grauer, Daniel Rabetti, Henry Updegrave
We provide an overview of crypto-related scams, including investment scams, Ponzi schemes, and more recently, rug pulls that are commonly seen in Decentralized Finance (DeFi) projects. We then discuss data sources for studying Initial Coin Offering (ICO) scams, before examining the case of PlusToken, the largest crypto scam, AnubisDAO, the prototypical rug pull, and Luno's anti-scam initiative, a good prototype for other cryptocurrency exchanges and service entities to follow. User protection and education are crucial in preventing scams, despite the fact that they may require efforts from centralized entities and regulators.