Blockchain Papers

Shailender Kumar Vats, Prasadu Peddi, Prashant Vats

No abstract is available for this record.

D. Saveetha, G. Maragatham, Vijayakumar Ponnusamy, Nemanja Zdravković

Blockchain networks serve as a transparent and secure ledger storage solution, yet they remain vulnerable to attacks. There must be some mechanism to protect the blockchain network from attacks. Among various attacks, the Distributed Denial of Service (DDoS) attack is considered severe, which is challenging to detect accurately and reliably. Machine learning techniques are used to detect the attack, which requires exploring all global attack data in a single system, which is difficult in practice. This article proposes a distributed machine learning mechanism called Federated Machine Learning for detecting the presence of DDoS attacks. But in federated machine learning the model itself can be poisoned by the malicious collaborating node which is another problem that this article solves by storing the model in blockchain and by introducing a new reputation-based miner selection procedure. The proposed framework integrates the federation of machine learning within the blockchain network framework for detecting DDoS attacks. Under the integrated framework, miners are used to train the blocks and they also participate in the machine learning training. A dynamic reputation-based miner selection mechanism that can balance exploration and exploitation is proposed for optimal miner selection, which can ensure the high accuracy of the machine learning model and improve the security of blockchain from attacks like DDoS attacks and 51% attacks. The proposed framework is tested with Random Forest, Multilayer Perceptron, and Logistic Regression machine learning algorithms. The proposed mechanism achieved maximum accuracy of 99.1% using random forest model which is superior to the existing mechanism of detection of DDoS attacks.

Yüksel Akay Ünvan, Cansu Ergenç

This study aims to find the best performing model in predicting cryptocurrencies using different machine learning models. In our study, an analysis was performed on various cryptocurrencies such as Aave, BinanceCoin, Bitcoin, Cardano, Cosmos, Dogecoin, Ethereum, Solana, Tether, Tron, USDCoin and XRP. Decision Trees, Random Forests, KNearest Neighbours (KNN), Gradient Boost Machine (GBM), LightGBM, XGBoost, CatBoost, Artificial Neural Networks (ANN), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Short Term Memory networks in Long Comparisons (LSTM) models were used. The performance of the models is compared with Mean Squared Error (MSE), Root Mean Square Error (RMSE) and Mean Absolute Error (MAE). The study results show that there is no single model that consistently outperforms others for all cryptocurrencies. Models such as XGBoost and Random Forests show consistent and strong performance across different cryptocurrencies, proving their robustness in this particular use case. Deep learning algorithms, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs) and Long Short Term Memory Networks (LSTMs), show significant accuracy in predicting some cryptocurrencies.

Jeyakumar Samantha Tharani, Zhé Hóu, Eugene Yugarajah Andrew Charles, Punit Rathore · 6 authors

Blockchain technology has been integrated into a wide range of applications in various sectors, such as finance, supply chain, health, and governance. However, the participation of a few actors with malicious intentions challenges law enforcement authorities, regulators and other users. These challenges revolve around dealing with an array of illegal activities such as asset trades in dark markets, receiving payments for cyber-attacks, and facilitating money laundering. Developing an efficient mechanism to identify malicious actors in blockchain networks is a pressing need to build confidence among the stakeholders and ensure regulatory adherence. The raw data of blockchain transactions do not readily reveal the dynamic behavioural changes and their interconnection between transactions and accounts. These behavioural patterns can be useful for identifying malicious actors. Machine Learning (ML)-based models for early warning and/or detection are considered one of the potential approaches. In ML, feature engineering plays a crucial role in enhancing the predictive performance of a model. This study proposes different categories of features and unified feature extraction approaches for raw Bitcoin and Ethereum transaction data and their interconnection information. As far as we are aware, there has been no study that considered a feature engineering approach for identifying malicious activities. The significance of the engineered features was validated against eight classifiers, including Random Forest (RF), XG-boost (XG), Silas, and neural network-based classifiers. The results showed that these features contribute to higher classification accuracy and higher Area Under the Receiver Operating Characteristic Curve (AUC) value for both Bitcoin and Ethereum transactions. This work also analysed the influence of engineered features in classification using the eXplainable Artificial Intelligence (XAI) technique SHapley Additive exPlanations (SHAP) values. The feature importance scores confirmed the significance of the proposed engineered features towards implementing classification models to identify, target and disrupt malicious activities in blockchain networks.

Jens Ernstberger, Stefanos Chaliasos, George Kadianakis, Sebastian Steinhorst · 8 authors

No abstract is available for this record.

Duc‐Minh Ngo, Dominic Lightbody, Andriy Temko, Colin C. Murphy · 5 authors

With the widespread integration of new technologies, IoT devices are becoming increasingly diverse and capable of handling highly complex tasks, compared to previous generations. This evolution has led to demands for a comprehensive security approach across multiple layers of an IoT architecture. This work proposes a scalable security solution from the edge to the cloud, combining Blockchain technology and anomaly-based Intrusion Detection Systems (IDSs). Smart contracts provide a transparent environment for registering and managing IoT devices on the cloud. Specifically, the smart contract includes two authorization levels for managing administrators and IoT devices. Besides, anomaly-based IDSs are deployed at Gateways to detect network attacks. We propose using lightweight machine learning models on FPGA hardware acceleration for Gateways. We have simulated the Blockchain network on the Ganache software, demonstrating that the smart contract effectively manages administrators and devices such that only authorized entities can access the system. The FPGA-based Gateway, which contains pre-trained Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) detection models from the IoT-23 dataset, has been deployed on the Alveo U280 card. The ANN model has achieved the highest processing speed at 20Gbps. The results indicate that integrating Blockchain and anomaly-based IDS significantly enhances scalable security in IoT networks.

Bahareh Parhizkari, Antonio Ken Iannillo, Christof Ferreira Torres, Sebastian Bănescu · 6 authors

No abstract is available for this record.

Aditya Srivastava

Present work explores the transformative potential of blockchain technology in cybersecurity.It begins with a fundamental introduction to blockchain's workings, then focuses on its current trends in bolstering cybersecurity, such as identity management and tamper-proof data storage.Real-world examples are used to guide practical implementations in industries like healthcare, finance, and voting.The paper also explores potential developments in the future, such as quantumresistant cryptography, decentralized autonomous organizations, and artificial intelligence integration.The paper concludes by assessing the lasting impact of blockchain on the broader cybersecurity landscape, highlighting its ability to reshape trust paradigms and empower individuals to control their digital identities.

Samson Kahsay Gebresilassie, Joseph Rafferty, Mamun Abu-Tair, Aftab Ali · 6 authors

The Internet of Things (IoT) is a technology paradigm that has transformed several domains including manufacturing, agriculture, healthcare, power grids, travel, and retail. Despite the enormous advantages that IoT offers to organizations and transforming individuals’ everyday lives in a wide range of domains, it comes with potential cyber risks that can negatively impact, harm, or damage them. Security is the most challenging issue in IoT systems due to insecure devices, inadequate IDMS, lack of data security and privacy, lack of trust, lack of risk analysis on network traffic, various vulnerabilities and attacks, lack of physical security, and many other risk factors. Although several security architectures have been developed, they fail to properly and fully address these IoT security challenges and an urgent demand awaits for a robust IoT security architecture. Thus, this work investigates state-of-the-art solutions and proposes a holistic novel IoT security architecture called SHIELD: Secure Holistic IoT Environment with Ledger-based Defense with core security capabilities of decentralized Identity Management System (IDMS), Network Traffic Monitoring, Analysis, and dataset generation, deep learning-based Intrusion Detection System (IDS), and Distributed Ledger Technology (DLT)-based Trust Management System (TMS). The proposed architecture is qualitatively compared with existing solutions using key features like a single point of failure, risk/attack-aware, trust, real-time traffic behavior monitoring, up-to-date dataset, cross-platform functionality, and availability among others. As a result of this comparison, SHIELD architecture provides a holistic and robust solution with multiple core security features to overcome some of the key security challenges IoT environment.

Uzma Jafar, Mohd Juzaiddin Ab Aziz, Zarina Shukur, Hafiz Adnan Hussain

As electronic voting systems become increasingly prevalent, the urgent need for robust security measures to combat evolving cyber threats has never been more critical. This paper introduces a ground-breaking architectural framework Secure-Tech Triad that synergistically combines Blockchain technology with Machine Learning (ML) algorithms and Internet of Things (IoT) capabilities to enhance the security and efficiency of electronic voting systems. This architectural framework utilizes a modified Proof-of-Stake (PoS) Blockchain algorithm, a Random Forest ML model for real-time anomaly detection, and an MQTT protocol for IoT-based data collection to create a more secure, efficient, and responsive voting environment. Rigorous testing and evaluation show that the integrated framework significantly outperforms existing Blockchain-only solutions in key performance indicators, such as security breach detection rate, system latency, and cost efficiency. This integrated approach is the best-performing model, achieving a 97% security breach detection rate, a 30% reduction in system latency (down to 2.3 seconds), and a 25% decrease in operational costs. These results underscore the combined effectiveness of Blockchain, AI, and IoT in enhancing security, speed, and cost-effectiveness. Specifically, the Random Forest algorithm has been instrumental in achieving an exceptional security breach detection rate, while IoT data collection has played a pivotal role in enabling real-time anomaly detection and proactive threat mitigation.

Njoku ThankGod Anthony, Mahmoud Shafik, Hany F. Atlam

With the proliferation of blockchain technology, ensuring the security and integrity of permissionless Proof-of-Stake (PoS) blockchain networks has become imperative. This paper addresses the persistent need for an effective system to detect and mitigate malicious nodes in such environments. Leveraging Deep Learning (DL) techniques, specifically Multi-Layer Perceptron (MLP), a novel model is proposed for real-time identification and detection of malicious nodes in PoS blockchain networks. The model integrates components for data collection, feature extraction, and model training using MLP. The proposed model is trained on labelled data representing both benign and malicious node activities, utilising transaction volumes, frequencies, timestamps, and node reputation scores to identify anomalous behaviour indicative of malicious activity. The experimental results validate the efficacy of the proposed model in distinguishing between normal and malicious nodes within blockchain networks. The model demonstrates exceptional performance in classification tasks with an accuracy of 99%, precision, recall, and F1-score values hovering around 0.99 for both classes. The experimental results verify the proposed model as a dependable tool for enhancing the security and integrity of PoS blockchain networks, offering superior performance in real-time detection and mitigation of malicious activities.

Shaoyu Li, Shanghao Shi, Yang Xiao, Chaoyu Zhang · 6 authors

No abstract is available for this record.

Rachael A. Ntongho, Joseph Lee

We explore how cybersecurity should be incorporated into corporate governance and develop a specific framework for implementing it. We consider different types of cybersecurity incidents, such as ransomware and data leaks, and their impacts on companies. We then discuss how cybersecurity situates in the current corporate governance theoretical framework. Based on the Resource Dependency Theory (RDT), we develop a specific governance framework with a focus on the role of chief cybersecurity officer, the audit committee, the regulatory powers, and market enforcement mechanisms. As more companies are becoming digital native and more services provided are digital in the Web3 space, this chapter is policy relevant as it provides a theoretical basis for implementing cybersecurity within companies in the digital space and a specific framework for the implementation.

Daniele Granata, Massimiliano Rak, Paolo Palmiero, Adele Pastena

Despite the growing role of information and communication technology (ICT) in public administration, paper ballots still dominate elections, especially in Italy. Electronic voting has had limited success worldwide, largely due to security and manipulation concerns. The COVID-19 pandemic has reignited interest in remote e-voting for safe participation while social distancing, though security remains a critical issue. Embracing electronic voting is essential to safeguard rights, improve resource efficiency, and promote digital citizenship. Accordingly, to address security concerns in e-voting, this research emphasizes the importance of security and legal measures. The study is based on ISO15408 (Common Criteria) certification process, a framework for independent security evaluations. The paper proposes a methodology that combines legal and technical requirements for e-voting security assessments, focusing on BPMN processes to model scenarios. The methodology has been applied to a common Ethereum smart contract, focusing on the e-voting process. A detailed analysis of a Solidity e-voting smart contract reveals its vulnerabilities and limitations. The research also produces a BPMN representation of an e-voting scenario, aligning logical behaviour with smart contract implementation. The aim is to bridge the gap between legal and technical aspects of e-voting, enhancing security and transparency.

Hao Sui, Jiale Zhang, Bing Chen, Di Wu · 6 authors

No abstract is available for this record.

Jerry Huang, Ken Huang

No abstract is available for this record.

Jerry Huang, Ken Huang, Krystal Jackson, Luyao Zhang · 5 authors

No abstract is available for this record.

Alvena Ehsan, Zahid Iqbal, Suhaila Abuowaida, Mohammad Aljaidi · 7 authors

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.

Jingjing Yang, Wenjia Yu, Jiajing Wu, Dan Lin · 6 authors

In recent years, phishing scams have emerged as one of the most serious crimes on Ethereum. Existing phishing scam detection methods typically model public transaction records on the blockchain as a graph, and then identify phishing addresses through manual feature extraction or graph learning frameworks. Meanwhile, these methods model transactions within a period as a static network for analysis. Therefore, these methods lack the ability to capture fine-grained time dynamics, and on the other hand, they cannot handle the large-scale and continuously growing transaction data on the Ethereum blockchain, resulting in lower scalability and efficiency. In this paper, we propose a two-dimensional streaming framework 2DynEthNet for Ethereum phishing scam detection. First, we cast the transaction series into 6 slices according to block numbers, treating each as a separate task. In the first dimension, we treat transaction features as edge features instead of node features within one task, allowing each transaction to be streamed in 2DynEthNet, aiming to capture the evolutionary features of the Ethereum transaction network at a fine-grained level in continuous time. In the second dimension, we adopt the strategy of incremental information training between tasks, which utilizes meta-learning to quickly update the model parameters under new slices, thus effectively improving the scalability of the model. Finally, experimental results on large-scale real Ethereum phishing scam datasets show that our 2DynEthNet outperforms the state-of-the-art methods with 28.44% average Recall and achieves the most efficient training speed, proving the effectiveness of both temporal edge representation and meta-learning. In addition, we provide an Ethereum large-scale dynamic graph transaction dataset, ETGraph, which aligns with the data distribution in real transaction scenarios without sampling and filtering unlabeled accounts.

Oluwaseun Priscilla Olawale, Sahar Ebadinezhad

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.

Lejun Zhang, Junjie Zhang, Kentaroh Toyoda, Yuan Liu · 7 authors

Bitcoin is widely used as the most classic electronic currency for various electronic services such as exchanges, gambling, marketplaces, and also scams such as high-yield investment projects. Identifying the services operated by a Bitcoin address can help determine the risk level of that address and build an alert model accordingly. Feature engineering can also be used to flesh out labeled addresses and to analyze the current state of Bitcoin in a small way. In this paper, we address the problem of identifying multiple classes of Bitcoin services, and for the poor classification of individual addresses that do not have significant features, we propose a Bitcoin address identification scheme based on joint multi-model prediction using the mapping relationship between addresses and entities. The innovation of the method is to (1) Extract as many valuable features as possible when an address is given to facilitate the multi-class service identification task. (2) Unlike the general supervised model approach, this paper proposes a joint prediction scheme for multiple learners based on address-entity mapping relationships. Specifically, after obtaining the overall features, the address classification and entity clustering tasks are performed separately, and the results are subjected to graph-based maximization consensus. The final result is made to baseline the individual address classification results while satisfying the constraint of having similarly behaving entities as far as possible. By testing and evaluating over 26,000 Bitcoin addresses, our feature extraction method captures more useful features. In addition, the combined multi-learner model obtained results that exceeded the baseline classifier reaching an accuracy of 77.4%.

Kainat Ansar, Mansoor Ahmed, Markus Helfert, Jungsuk Kim

In cybersecurity, personal data breaches have become one of the significant issues. This fact indicates that data breaches require unique detection systems, techniques, and solutions, which necessitate the potential to facilitate precise and quick data breach detection. Various research works on data breach detection and related areas in dealing with this problem have been proposed. Several survey studies have been conducted to comprehend insider data breaches better. However, these works did not examine techniques related to blockchain and innovative smart contract technologies to detect data breaches. In this survey, we examine blockchain-based data breach detection mechanisms developed so far to deal with data breach detection. We compare blockchain-based data breach detection techniques based on type, platform, smart contracts, consensus algorithm language/tool, and evaluation measures. We also present a taxonomy of contemporary data breach types. We conclude our study by outlining existing methodologies’ issues, offering ideas for overcoming those challenges, and pointing the way forward.

Shatha Alharbi, Daniyal Alghazzawi, Abeer Hakeem, Linda Mohaisen · 6 authors

Nowadays, the Internet of Things (IoT) has become immensely popular in various fields like healthcare, smart cities, and industrial automation. IoT networks are expanding rapidly, including different IoT devices with limited capabilities in terms of power and storage which make the IoT security a crucial issue. IoT Network Intrusion Detection System is one of the most famous solutions that used to identify different types of attack and extract their features (e.g. IP addresses of attackers). The IP address is a valuable feature that can identify malicious traffic of an attacker who attempts to access the IoT network. However, IoT Network Intrusion Detection Systems has different limitations: centralization and scalability which easily allow attackers to access the IoT network. Accordingly, this paper aims to address these issues by proposing a novel collaborative framework called Blockchain-based Collaborative Intrusion Detection Systems (BC-IDSs) that utilizes Blockchain technology to connect several IDSs. The BC-IDSs framework (1) creates a list of malicious IP addresses using IDSs; (2) utilizes Blockchain to share and store the Blacklist; (3) creates a function for duplication check in the Blockchain layer. Further, the implementation of a proof of concept for BC-IDSs framework is presented by using Ethereum Blockchain simulators. Compared to previous works, this paper discusses several types of performance metrics that prove BC-IDSs is able to secure IoT networks. BC-IDSs also increases the scalability by 50% when compared to one of the previous defence work.

Nan Sun, Wei Wang, Yongxin Tong, Kexin Liu

No abstract is available for this record.