Blockchain Papers

Deepak Kaul

No abstract is available for this record.

Mantri Christ Elison, Martin Victor K, Gifton Paul Immanuel

The objective of this research is to develop an R&D (Research and Development) for the hardiness relay alert system, including applying the machine learning, and the fuzzy logic networks for the real time Ethereum transaction 'match failure' detection and the improved Ethereum blockchain security.As an example, the system is computing on the transactions due to the fact the system for transaction analysis corresponds with concrete intrinsic characteristics and thus it mainly takes out suspicious or malicious transactions.The logistic regression, support vector machines (SVM) decision tree and random forests are used in this research and optimized by grid search.Finally, on the other hand, uncertainty problems and false alarms are solved where fuzzy membership functions are used to put transaction attributes into linguistic hobbled variables (such as 'low', 'medium' and 'high').The conclusion of this descriptive research is that fuzzy logic integration with machine learning can improve the approach of anomaly mediation compared to the rules based approach and it is superior to rules based approach.Finally, the effectiveness of the models is detailed and replicated in various graphical representations of the decision making process and membership functions to show that the system can be deployed in real time to secure blockchain networks.

Mosbah Alown, Mehmet Sabır Kiraz, Muhammed Ali Bingöl

Electronic voting (e-voting) systems have significantly improved the traditional voting process by addressing key concerns such as security, public acceptability, and convenience. However, these systems often face unique challenges, such as ensuring voter privacy and verifiability, preventing coercion and double voting, and maintaining scalability while protecting participant confidentiality. This study critically analyses and compares various e-voting schemes and technologies, evaluating their security features, verifiability mechanisms, and potential vulnerabilities. This paper reviews Direct Recording Electronic (DRE) voting, internet voting, and blockchain-based e-voting systems. In so doing, we provide an understanding of cryptographic primitives employed in e-voting systems and how they address specific characteristics and challenges associated with each voting scheme. Furthermore, we examine the applications proposed by previous studies in the context of these voting systems, assessing their strengths, limitations, and impact on democratic procedures. The cryptographic primitives reviewed include techniques like homomorphic encryption, blind signatures, and zero-knowledge proofs, which can enhance voter privacy, verifiability, and resistance to coercion and double voting.

Mridul Kumar Gupta, Mradul Singh Tawar, Mrityunjay Kumar

No abstract is available for this record.

Terrence August, Duy Dao, Kihoon Kim, Marius Florin Niculescu

Cryptocurrencies have prompted a shift away from classic security attacks toward ransomware-based extortion. To better understand the impact of cryptocurrencies on the cybersecurity landscape, we conduct a comparative analysis of cybersecurity metrics prior to and after the adoption of cryptocurrency using a series of connected software-use models in the presence of security externalities. In this framework, we endogenize the actions of both heterogeneous consumers and attackers, with entry of the latter being driven by both the size of the unpatched consumer population and, as a subset of it, the size of the ransom-paying consumer population. We first examine users’ adoption and patching behavior under both security scenarios. We explore how changes in attacker entry costs impact outcomes under both conventional and post-crypto ransomware threat landscapes. We show that ransomware scenarios may be more desirable than conventional ones when attacker entry costs are low, provided that the gains from entering with standard attacks under the ransomware scenario are not too high. However, under such scenarios, social welfare can increase under the same conditions that lead to larger ransoms being demanded and a higher expected total ransom being paid, which presents a conundrum to policymakers. We also examine the impact of market parameters associated with security losses from conventional attacks and residual losses when victims pay in ransomware attacks. This paper was accepted by Kay Giesecke, finance. Funding: This work was partially supported by Insung Research Grant of KUBS, the LG Yonam Foundation (of Korea), and an award from the Georgia Institute of Technology Center of International Business Education and Research as part of its funded research program. Supplemental Material: The online appendices are available at https://doi.org/10.1287/mnsc.2023.00969 .

Xiaoqi Li, Wenkai Li, Zhiquan Liu, Yuqing Zhang · 5 authors

Decentralized finance (DeFi) protocols are crypto projects developed on the blockchain to manage digital assets. Attacks on DeFi have been frequent and have resulted in losses exceeding $80 billion. Current tools detect and locate possible vulnerabilities in contracts by analyzing the state changes that may occur during malicious events. However, this victim-only approaches seldom possess the capability to cover the attacker’s interaction intention logic. Furthermore, only a minuscule percentage of DeFi protocols experience attacks in real-world scenarios, which poses a significant challenge for these detection tools to demonstrate practical effectiveness. In this paper, we propose DeFiTail, thefirstframework that utilizes deep learning technology for access control and flash loan exploit detection. Through feeding the cross-contract static data flow, DeFiTail automatically learns the attack logic in real-world malicious events that occur on DeFi protocols, capturing the threat patterns between attacker and victim contracts. Since the DeFi protocol events involve interactions with multi-account transactions, the execution path with external and internal transactions requires to be unified. Moreover, to mitigate the impact of mistakes in Control Flow Graph (CFG) connections, DeFiTail validates the data path by employing the symbolic execution stack. Furthermore, we feed the data paths through our model to achieve the inspection of DeFi protocols. Comparative experiment results indicate that DeFiTail achieves the highest accuracy, with 98.39% in access control and 97.43% in flash loan exploits. DeFiTail also demonstrates an enhanced capability to detect malicious contracts, identifying 86.67% accuracy from the CVE dataset. By monitoring existing contracts, we identified five distinct categories of vulnerabilities: repetition abuse, unsafe unintended exploitation, signature violated exploitation, insecure interfaces exploitation, and unrestricted token transfer.

Zubaida Rehman, Iqbal Gondal, Hai Dong, Mengmeng Ge · 6 authors

Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereum’s consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereum’s 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.

Yu Zhang

No abstract is available for this record.

Vasavi Chithanuru, Mangayarkarasi Ramaiah

Blockchain is a prominent and leading decentralized ledger technology that has gained global attention and adoption across various industries. Long-range attacks (LRAs) are when an adversary attempts to rewrite the blockchain’s history from a point far back in time. Since PoS Blockchain relies on validators’ stakes as a form of security, LRAs can potentially undermine the network’s security if not detected and prevented. In order to protect against long-range attacks, this research suggests a high-performance explainable neural network model that can accurately categorize nodes as malicious or non-malicious while maintaining interpretability. The proposed explainable neural network model includes Residual Auto Encoder (RAE) guided generalized additive models with incorporating structured interactions (RAE-GAMI-Net) for LRA detection in PoS Blockchain In this work, a wrapper-based Binary Orchard Algorithm (W-BOA) is used to find the best features to lessen the dimensionality of extracted Characteristics, and a global feature extraction has been implemented based on multi-scale Densenet (MDensenet) that assures early convergence and optimal performance by providing global optimal solution. Then, the transformed features are used to train the RAE-GAMI-Net-based model to detect the LR attack. The included RAE learns a compressed representation (latent) of the input features. Then, the latent features are classified with GAMI-Net, balancing the model interpretability and accuracy. The effectiveness of our proposed method is assessed using the Proof of Stake blockchain dataset and benchmarked against other deep learning techniques. Our approach yields significant enhancements in accuracy (0.962), precision (0.9614), and recall 0.9604, accompanied by a notably low Brier score of 0.038.

Bhupendra sahu

No abstract is available for this record.

Chen Zhang, Su Peng

Developers and users are drawn to Ethereum due to its rapidly growing asset count. However, potential vulnerabilities and malicious behaviors during the execution of smart contracts have led to an increasing demand for security detection technology. Conventional static and dynamic analysis methods are less useful in the case of complex opcode sequences and multiple execution paths. To tackle this problem, this paper proposes an Ethereum intrusion detection method based on Bidirectional Long Short-Term Memory (Bi-LSTM) network with multi-head attention. It examines the opcode execution paths generated from the intra-and-inter-function Control Flow Graphs (CFGs) using the EPP algorithm and captures the rich feature representations and long dependencies. This combination increases the precision and efficacy of detecting malicious activity and smart contract vulnerabilities while simultaneously enhancing the model’s robustness and interpretability and handling variable-length sequences. For the five selected vulnerabilities, the precision, recall and F1-score of this model are above 89.9%, 87.3%, and 88%, respectively.

Christian Delgado‐von‐Eitzen, Manuel J. Fernández Iglesias, Luis Anido, Fernando A. Mikic-Fonte

No abstract is available for this record.

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.

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.

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.

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%.

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.

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.

Padmavati Shrivastava

Blockchain technology has emerged as a transformative solution for securing distributed networks, offering decentralized and immutable data management. However, the resilience of blockchain systems faces challenges from various security threats, including double-spending, Sybil attacks, and vulnerabilities in smart contracts. This paper explores the effectiveness of various blockchain security protocols in enhancing the security and stability of distributed networks. The study provides a comprehensive review of cryptographic techniques, consensus algorithms, and privacy-enhancing technologies, such as Zero-Knowledge Proofs and Multi-Party Computation. Through a detailed analysis of case studies involving Bitcoin, Ethereum, and Hyperledger Fabric, the paper highlights the strengths and limitations of different security protocols. Additionally, the paper discusses the future direction of blockchain security, including the impact of emerging threats such as quantum computing on current security measures. The findings emphasize the need for ongoing innovation in security protocols to ensure the long-term resilience of blockchain networks. The paper concludes with recommendations for improving the security frameworks in both public and permissioned blockchains, with a focus on scalability, privacy, and resistance to emerging attacks.

Emrullah Şahin, Naciye Nur Arslan, Fırat Aydemir

The Industrial Internet of Things (IIoT) refers to a structure where multiple devices and sensors communicate with each other over a network. As the number of internet-connected devices increases, so does the number of attacks on these devices. Therefore, it has become important to secure the data and prevent potential threats to the data in factories or workplaces. In this study, a deep learning-based architecture was used to determine whether the data collected from IIoT sensors was under attack by looking at network traffic. The data that was not exposed to attacks was stored on the Ethereum Blockchain network. The Ethereum blockchain network ensured that sensor data was stored securely without relying on any central authority and prevented data loss in case of any attack. Thanks to the communication process over the blockchain network, updating and sharing data was facilitated. The proposed deep learning-based intrusion detection system separated normal and anomaly data with 100% accuracy. The anomaly data were identified with an average of 95% accuracy for which attack type they belonged to. The data that was not exposed to attacks was processed on the blockchain network, and an alert system was implemented for the detected attack data. This study presents a method that companies can use to secure IIoT sensor data.

Scott Seidenberger, Anindya Maiti

We hypothesize that peer-to-peer (P2P) overlay network nodes can be attractive to attackers due to their visibility, sustained uptime, and resource potential. Towards validating this hypothesis, we investigate the state of active reconnaissance attacks on Ethereum P2P network nodes by deploying a series of honeypots alongside actual Ethereum nodes across globally distributed vantage points. We find that Ethereum nodes experience not only increased attacks, but also specific types of attacks targeting particular ports and services. Furthermore, we find evidence that the threat assessment on our nodes is applicable to the wider P2P network by having performed port scans on other reachable peers. Our findings provide insights into potential mitigation strategies to improve the security of the P2P networking layer.

Chong-Gee Koa, Swee‐Huay Heng, Ji‐Jian Chin

This paper explores the critical role of Public Key Infrastructure (PKI) in ensuring the security of electronic transactions, particularly in validating the authenticity of websites in online environments. Traditional Centralised PKIs (CPKIs) relying on Certificate Authorities (CAs) face a significant drawback due to their susceptibility to a single point of failure. To address this concern, Decentralised PKIs (DPKIs) have emerged as an alternative. However, both centralised and decentralised approaches encounter specific challenges. Researchers have made several attempts using blockchain-based PKI, which implements a reward and punishment mechanism to enhance the security of traditional PKI. Most of the attempts are focused on CA-based PKI, which still suffers from the risk of a single point of failure. Inspired by ETHERST, which is a blockchain-based PKI that implements Web of Trust (WoT) with reward and punishment, we introduce ETHERST version 3.0, with improvements in its secure level algorithm that enhances trustworthiness measurement. Comparative simulations between ETHERST version 2.0 and ETHERST version 3.0 reveal the superior performance of the latter in trustworthiness measurement and ensure the higher security of a virtual community. The new simulation algorithm with different node type definitions and assumptions presents results through tables and graphs, showing that ETHERST version 3.0 outperforms ETHERST version 2.0. This research contributes to advancing the field by introducing an innovative PKI solution with enhanced trustworthiness and security features. • Comparison of blockchain-based PKIs which implement reward and punishment mechanism. • Reward and punishment with blockchain-based PKI with an improved new algorithm. • Definition of bad( B ), normal( N ) and good( G ) nodes to improve simulations algorithm.