Follow blockchain research across journals, conferences, and preprint repositories.
1,269 results · page 11 of 53
Christian Delgado‐von‐Eitzen, Manuel J. Fernández Iglesias, Luis Anido, Fernando A. Mikic-Fonte
No abstract is available for this record.
성신여자대학교/융합보안공학과, Nam-Ryeong Kim, Dong-Ju Ryu, Il-Gu Lee
스마트 컨트랙트의 활용이 증가함에 따라 이를 복제하고 수정하는 과정에서 다양한 보안 문제가 발생하고 있다. 본 연구는 스마트 컨트랙트의 변경 사항을 효과적으로 감지하고 공격 표면을 식별할 수 있는 프레임워크를 제안한다. 제안된 방법론은 AST(Abstract Syntax Tree), CFG(Control Flow Graph), GNN(Graph Neural Network)을 통합적으로 활용하여 코드 구조를 심층 분석한다. 실증 연구로서 Uniswap V2 프로토콜과 이를 기반으로 파생된 프로젝트들의 보안 위험성을 평가하였으며, 특 히 Uranium Finance와 BurgerSwap 사례를 중심으로 코드 유사도 분석과 보안 취약점 진단을 수행하였다. 이를 통해 제안된 분석 체계가 스마트 컨트랙트의 보안 감사에 적용될 가능성을 평가하였다.
Antonio Pellicani, Gianvito Pio, Sašo Džeroski, Michelangelo Ceci
No abstract is available for this record.
Sparsh Kasana, Monika Sharma, Shashnak Gupta
Decentralized and distributed systems, like those based on Blockchain technology, are vulnerable to a form of attack called as the Sybil attack. It leads to initiate acute effect as well as initiate numerous other attacks like Denial of Service, Distributed Denial of Service, and majority attack etc. In Sybil attack, an adversary or a malicious user creates multiple fake “identities” in the system. Preventing Sybil attacks is a difficult in systems without a central trusted node. Various protocols based on social networks have been proposed by the research community to mitigate the influence of malicious nodes creating multiple identities, which differ in multiple ways in their approaches and guarantees. We explore these protocols in depth, including their assumptions, procedure, and results. Bitcoin and other traditional blockchain architectures use protocols like Proof of Work and Proof of Stake to make Sybil attacks expensive and impractical. This article provides a comprehensive survey of such techniques as applied in various cryptocurrencies.
Iim Abdurrohim, Badie Uddin, Subhanjaya Angga Atmaja, Asep Saeful Millah · 5 authors
Article investigates a blockchain-based framework for enhancing data security in Internet of Things (IoT) systems. Employing a qualitative research methodology, the study explores the integration of blockchain technology to address vulnerabilities in IoT ecosystems, including data breaches, unauthorized access, and the challenges of centralized data storage. By analyzing existing literature, case studies, and expert opinions, the research identifies blockchain's potential to provide secure, decentralized, and immutable data management in IoT systems. The findings highlight blockchain's ability to enhance data integrity through distributed ledgers, ensure data confidentiality via advanced cryptographic techniques, and improve accountability with transparent transaction records. Additionally, the research underscores the scalability challenges of blockchain in IoT, proposing hybrid architectures that combine private and public blockchain systems to optimize performance and resource utilization. Real-world applications such as smart home systems, healthcare IoT, and industrial IoT demonstrate the practical viability of blockchain integration for improving security. The study also emphasizes the importance of regulatory frameworks and cross-industry collaboration to address interoperability and privacy concerns. This research contributes to the growing discourse on secure IoT infrastructure by presenting a comprehensive blockchain-based security framework. The proposed framework offers actionable insights for IoT developers, researchers, and policymakers seeking to enhance trust, reliability, and resilience in IoT systems.
Achraf Yahia, Yassine Mouhssine, Abdelkader El Alaoui, Saïd Ouatik El Alaoui
No abstract is available for this record.
Erol Kına, Emre BİÇEK
Bitcoin is the most valuable cryptocurrency and is renowned for its rapid and volatile price fluctuations in comparison to other currencies. This offers potential for the prediction of Bitcoin prices and has attracted the interest of researchers. Twitter (X) is one of the most widely used social media platforms. The aim of this study is to analyse the sentiment expressed in comments about bitcoin on the social media platform X using a variety of machine learning algorithms. A variety of machine learning techniques are used to classify user sentiment towards bitcoin. Moreover, the efficacy of standard bag-of-words and term frequency-inverse document frequency (TF-IDF) methods is evaluated in comparison with machine learning approaches for the purpose of expressing text as numerical vectors. Finally, a keyword ranking was performed to determine the importance of each sentiment in the development of cryptocurrencies. The bag-of-words and TF-IDF methods were used, which facilitate the representation of text-based data. The best result was obtained with the decision trees algorithm (98.74% accuracy) using the TF-IDF method. The bag-of-words method was found to produce better results in general.
Rui Xing, Zhou Su, Yuntao Wang
As the Internet of Vehicles (IoV) advances, the security concerns surrounding vehicular networks have grown increasingly critical due to the openness of networking among vehicles, inadvertently creating more opportunities for adversaries to infiltrate and potentially disrupt vehicle operations. Intrusion detection systems (IDSs) stand as a promising solution, effectively mitigating the myriad of threats and security concerns that plague vehicles. In this article, we delve into the realm of IDSs within vehicular networks and propose an innovative collaborative intrusion detection framework based on blockchain technology and auction game. First, we integrate a vehicular blockchain into the IDS, offering a holistic approach to tackling both internal and external threats within vehicular networks. Second, we introduce a novel assistant-delegated Byzantine fault tolerance (A-DBFT) consensus algorithm, designed to bolster the efficiency of intrusion detection within the blockchain while maintaining the efficacy of the consensus mechanism. Third, we develop an auction game mechanism that incentivizes assistants and verifiers to actively initiate and participate in auctions, thereby enhancing the overall security of our intrusion detection scheme. Ultimately, we present simulation results that validate the superiority of our proposed scheme compared to conventional approaches.
Saikat Samanta, Achyuth Sarkar
No abstract is available for this record.
Mohamed A. Fouly, Taysir Hassan A. Soliman, Ahmed I. Taloba
A blockchain is made up of an ordered list of nodes connected by links known as chains. The nodes in the blockchain store data and are stored together. The distributed and decentralized ledger technology, blockchain, empowers cryptocurrencies like Bitcoin and Ethereum. It makes a safe and open record of transactions by permitting the distribution of digital data as a “block” but prohibiting its duplication. Furthermore, blockchain-based anomaly detection tools, which always automatically detect and weed out abnormal behaviors, are essential for protecting networks and systems from unforeseen intrusions. A smart contract could monitor real-time transaction volumes, access patterns, or resource usage. If anomalies are detected, such as unusual spikes in activity, the smart contract can trigger alerts or take predefined actions. Numerous anomaly detection analysis techniques have been put out and used in the scientific literature in various fields. This paper provides an overview of the latest machine learning techniques for identifying abnormal behaviors in blockchain, such as supervised, unsupervised, and deep learning. We also go over a few of the applications for anomaly behaviors detection.
Aswani Devi Aguru, Amrit Pandey, Suresh Babu Erukala, Ali Kashif Bashir · 7 authors
Routing protocol for low-power and lossy network (RPL) is a routing protocol for resource-constrained Internet of Things (IoT) network devices. RPL has become a widely adopted protocol for routing in low-powered device networks. However, it lacks essential security features, including end-to-end security, robust authentication, and intrusion detection capabilities. Blockchain is a decentralized and immutable digital ledger that records transactions across multiple computers. It provides privacy, transparency, security, and trust. In this work, we proposed a blockchain-based reliable RPL protocol called reliable-RPL, which uses node reliability, link reliability, and relative trust scores of RPL-enabled IoT devices. The parent selection and network topology formulation are based on the proposed reliability-aware objective function. A lightweight ECC-based scheme performs registration, identification, and authentication of RPL-enabled IoT devices. The consistent topological updates from these authenticated IoT devices are used to secure routing paths in RPL-enabled networks. Using a modified trickle algorithm, we employed a reputation-based trust system that monitors and labels malicious nodes based on their reliable activities. The novelty of the proposed framework relies on integrating Contiki-NG (as fronted for IoT network simulation) and Hyperledger Fabric (as a backend for blockchain-based device authentication and trust-based attack resilience regarding rank, replay, sinkhole, and route poisoning attacks). The experimental evaluation of reliable-RPL has demonstrated its effectiveness compared to state-of-the-art methods regarding significant performance metrics, including packet loss, routing overhead, and throughput on Hyperledger Caliper.
Anastasia Kassiani Blitsi, Eleftheria Katsoura, Georgios Stavropoulos, Konstantinos Votis
The rise of blockchain technology and cryptocurrencies such as Bitcoin and Ethereum has created new avenues for both lawful and illicit activities, including illegal firearm transactions. This study applies a combination of graph-based analysis, functional data techniques, and machine learning to detect and classify suspicious activities related to firearm trafficking on blockchain networks. A Random Forest model, achieving a precision of 0.907 and recall of 0.786, was used to identify illicit Bitcoin addresses, while a multi-target classifier categorized these addresses by specific types of illicit activity. For Ethereum, an XGBoost model achieved a precision of 0.9864 and an accuracy of 0.9901, demonstrating robust detection of suspicious accounts. Feature engineering and a rule-based system further enhanced model performance, though challenges remain in addressing misclassifications, particularly in distinguishing subtle transaction patterns. These findings underscore the potential of machine learning in blockchain forensics, providing critical insights for law enforcement efforts to combat illegal firearm trading.
Ohood Alharbi, Riaz Ahmed Shaikh, Rameez Asif
Intrusion Detection Systems (IDS) are the key for securing the rapidly evolving Internet-of-Things (IoT), where data security and privacy will become increasingly important in the forthcoming era. This research presents an innovative method for improving IDS performance through the integration of Artificial Intelligence (AI), Blockchain, and Digital Twin (DT) technologies. AI is utilized for real-time anomaly detection, whereas DT replicate device behavior for predicting threats and Blockchain ensures secure, decentralized data transmission. Energy-efficient zero-knowledge proofs are employed to meet the energy requirements of Blockchain, enhancing both security and resource efficiency. The performance of the suggested system will be assessed based on detection accuracy, latency, scalability, energy efficiency, and privacy preservation. This distinctive integration of advanced technologies delivers a multi-faceted security system, providing a thorough respond to for strengthening security in IoT networks.
Pranjali Ulhe, Suresh S. Asole
The rapid deployment of 5G networks necessitates the development of secure, scalable, and efficient Internet of Vehicles (IoV) systems. Existing IoV solutions often struggle with real-time threat detection, scalability, efficient resource allocation, and privacy preservation. This work proposes an integrated framework leveraging blockchain technology, AI-driven anomaly detection, dynamic network slicing, and secure multi-party computations. We introduce AI-Driven Anomaly Detection and Mitigation (ADAM) to identify and respond to security threats in real-time. Utilizing Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), ADAM analyzes network traffic data to detect anomalies with a detection accuracy of 95%, a false positive rate of 2%, and an average response timestamp of 50 ms. To tackle scalability and latency issues inherent in traditional blockchain systems, we propose Edge-Based Blockchain Sharding (EBBS).The innovative use of a modified Proof-of-Stake (PoS) mechanism tailored for edge environments further enhances the scalability of the IoV system. AI-Enabled Dynamic Network Slicing (ADNS) is implemented to optimize resource allocation based on real-time traffic demands and QoS requirements. Finally, we incorporate Secure Multi-Party Computation for Collaborative Data Processing (SMPC-CDP) to enable secure, privacy-preserving data analysis among IoV entities ensuring privacy with a computation overhead of 20%, and data utility preservation of 95%.
Dhanasak Bhumichai, Ryan Benton
An eclipse attack is a strategy where attackers control communication between nodes in peer-to-peer networks, such as Ethereum, using compromised nodes to escalate further attacks. Given the vast and complex nature of big data in Ethereum networks, detecting these attacks is challenging. This paper aims to identify effective features for eclipse attack detection by analyzing large volumes of network traffic data. We simulate an Ethereum network, conducting eclipse attacks to generate datasets where 28% of the traffic consists of malicious packets. We apply five feature extraction methods—common network traffic, Entropy, φ-Divergence, packet communication statistics, and packet characteristics statistics—leveraging big data analysis techniques to process and refine extensive traffic data. To address the challenges posed by imbalanced and overlapping data, SMOTE and Tomek link algorithms are used, and Mutual Information selects the most significant features to enhance classifier performance. We evaluate five machine learning models, including XGBoost, kNN, and Random Forest, finding that XGBoost achieves the highest performance, with 99.25% accuracy and a computational time of 184 ms when processing the top 25 features, which indicates real-time detection could be possible.
Stanisław Barański, Ben Biedermann, Joshua Ellul
Voting is a cornerstone of collective participatory decision-making in contexts ranging from political elections to decentralized autonomous organizations (DAOs). Despite the proliferation of internet voting protocols promising enhanced accessibility and efficiency, their evaluation and comparison are complicated by a lack of standardized criteria and unified definitions of security and maturity. Furthermore, socio-technical requirements by decision makers are not structurally taken into consideration when comparing internet voting systems. This paper addresses this gap by introducing a trust-centric maturity scoring framework to quantify the security and maturity of seventeen internet voting systems. A comprehensive trust model analysis is conducted for selected internet voting protocols, examining their security properties, trust assumptions, technical complexity, and practical usability. In this paper we propose the Internet Voting Maturity Framework (IVMF) which supports nuanced assessment that reflects real-world deployment concerns and aids decision-makers in selecting appropriate systems tailored to their specific use-case requirements. The framework is general enough to be applied to other systems, where the aspects of decentralization, trust, and security are crucial, such as digital identity, Ethereum layer-two scaling solutions, and federated data infrastructures. Its objective is to provide an extendable toolkit for policy makers and technology experts alike that normalizes technical and non-technical requirements on a univariate scale.
Viktoriia Semerenska
The decentralized finance (DeFi) ecosystem experienced significant growth in 2024, accompanied by a rise in sophisticated cyberattacks. This article analyzes key security incidents, including the PenPie reentrancy attack, flash loan exploits on Radiant Capital and Goledo Finance, a social engineering breach at Concentric Finance, a multi-signature compromise on Orbit Chain, and phishing campaigns targeting Binance users. Detailed technical insights and countermeasures, such as reentrancy guards, decentralized oracles, and enhanced user authentication, highlight strategies for mitigating risks and strengthening DeFi security.
Jianrong Wang, Mingyu Li, Dengcheng Hu, Xiulong Liu · 7 authors
Phishing scams represent a significant criminal activity on Ethereum, driving the need for effective detection methods. The methods based on graph neural networks(GNNs) make significant breakthroughs due to their ability to model complex transaction networks. However, existing approaches often overlook the heterogeneity of Ethereum’s transaction graph during neighbor nodes aggregation. These methods typically focus on low-order neighbors, disregarding high-order ones, which limits their overall performance. To this end, we propose the High- and Low-order Transaction Aggregation Graph Network(HLTAG), which separately aggregates high- and low-order features for more effective feature representation. Specifically, we utilize biased random walk to aggregate low-order neighbors. We employ path aggregation to handle high-order neighbors. To mitigate the influence of noise and redundant information from high-order neighbors, we introduce a combination of attention decay, node similarity, and path attention mechanism, which dynamically adjust the aggregation weights. Extensive experiments demonstrate that HLTAG (94.4% Recall and 89.3% AUC) outperforms the state-of-the-art approaches in detecting Ethereum phishing scams, and exhibits significant advantages in large-scale scenarios.
V. Chevardin
The paper presents the main approaches to the construction of the PKI public key architecture divided into basic, two-level, and multi-level hierarchies. Modern methods of attacks on existing public key infrastructures, protocols for building secure connections of both wired and wireless systems are considered. The basics of the class of attacks on PKI infrastructures are defined, of which the main attention is paid to the most dangerous class of attacks – man-in-the-middle (MITM-attacks). The paper provides models of various classes of MITM attacks, their details and existing methods of reducing the risks of their implementation. Existing examples of successful attacks on enterprises and various organizations that implemented MITM attack models at the application, network, and physical levels of the network interaction model are also given. For the PKI infrastructure, one of the options is its segmentation, which allows to reduce the scope of attacks on the key certification center. The paper also provides an alternative way to protect against MITM attacks using distributed micro ledger technology (DLT) to create a decentralized cryptographic key distribution system (DKMS). The solution is based on the use of micro ledgers (distributed ledger technology – DMLT). Using DMLT to create a DKMS allows protection against additional classes of MITM attacks.
Jugnu Misal -
The integration of blockchain technology into automated incident management systems represents a significant advancement in securing and validating system logs and incident records. This article presents a comprehensive article analysis of blockchain's application in incident management, examining its role in creating immutable audit trails and enhancing security controls. Through systematic review of implementation patterns and industry case studies, the article explores how distributed ledger technology addresses traditional challenges in log integrity and incident response validation. The article investigates the architectural frameworks necessary for successful blockchain integration, including considerations for scalability, performance, and regulatory compliance. The findings demonstrate that blockchain-based incident management systems offer enhanced transparency, improved audit capabilities, and robust security measures compared to traditional approaches. Additionally, the article examines emerging patterns in enterprise adoption, implementation challenges, and the synergies between blockchain and other emerging technologies in the incident management landscape. This article contributes to the growing body of knowledge on blockchain applications in enterprise security operations and provides a framework for organizations considering blockchain adoption for their incident management processes. The article concludes with recommendations for implementation and identifies areas for future research in this rapidly evolving field.
Brij B. Gupta, Akshat Gaurav, Razaz Waheeb Attar, Varsha Arya · 7 authors
No abstract is available for this record.
Sam Goundar
The rapid evolution of cyber threats, driven by artificial intelligence (AI) and machine learning (ML), has exposed critical gaps in traditional cybersecurity frameworks, particularly in real-time threat detection and response. This paper presents research on the Next-Gen Cyber Security Sentinel, a system that integrates AI algorithms with blockchain-based smart contracts to detect and mitigate sophisticated, AI-powered cyberattacks in real-time. Through the use of Software-Defined Networks (SDN), the system simulates complex attack scenarios, providing flexible and scalable network management. Penetration testing confirmed the system's ability to detect, respond to, and mitigate advanced cyber threats, ensuring enhanced protection of critical infrastructure. The findings were both novel and innovative, demonstrating that the integration of AI and blockchain technology significantly improves the speed and accuracy of real-time threat detection. This research contributes to the field of cybersecurity by offering a robust, scalable solution to counter emerging AI-driven attacks, particularly in critical sectors such as smart cities and Industry 4.0 environments. These findings offer crucial insights for advancing cybersecurity solutions in the face of rapidly evolving, AI-driven cyber threats. The AI-Blockchain platform achieved a 95% detection rate with a 2% false positive rate and maintained blockchain transaction latency under 200 milliseconds, demonstrating significant improvements in real-time threat detection and response capabilities.
Xie Nannan, Mu Linyang, Wang Yangfan, Ma Yubo
No abstract is available for this record.
Shengyu Chen, Shenghao Jin, Yigang Wei, Hui Zhang
This study mainly focus on Sybil attacks with the Identity-Augmented Proof-of-Stake (IdAPoS) protocol under different network topologies, including random, scale-free, and hierarchical networks. The study finds that scale-free networks are more resistant to Sybil attacks, delaying their effects. Furthermore, the research improves the IdAPoS protocol by introducing active strategies for honest nodes, which improves the behaviours of this protocol, and by enabling them to dynamically assess and update their trusted and suspicious authority lists based on on-ledger data, which improves the completeness of this protocol.