Follow blockchain research across journals, conferences, and preprint repositories.
2,015 results · page 23 of 84
Prakash Yadav, Harsh Raj, Sahej Gautam, Ankesh Tripathi
No abstract is available for this record.
Chahrazad Adouane, Sonia Sabrina Bendib, Hamouma Moumen
No abstract is available for this record.
Meng Shen, Xiangyun Tang, Wei Wang, Liehuang Zhu
No abstract is available for this record.
Jong‐Moon Chung
No abstract is available for this record.
Rodney Garratt, Maarten R.C. van Oordt
No abstract is available for this record.
P. Infant Vinoth, D. Nagendra Kumar, M. P. S. Guhan, M Archana · 5 authors
No abstract is available for this record.
Ananda Ravuri, M. Sadish Sendil, Moshe Rani, A. Srikanth · 7 authors
Protection of the Internet of Things (IoT) has become a significant concern due to the widespread use of IoT technologies. Conventional Intrusion Detection Systems (IDS) have challenges when used in IoT networks because of resource restrictions and complexities. Blockchain Technology (BCT) has significantly altered organizations' financial behavior and effectiveness in recent years. Data security and system stability are crucial concerns that must be tackled in blockchain systems. The study suggests a mechanism called Deep Blockchain-Enabled Collaborative Anomaly Detection (DBC-CAD) for security-focused distributed Anomaly Detection (AD) and privacy-focused BC with smart contracts in IoT networks. A Modified - Long Short-Term Memory (M-LSTM) based Deep Learning (DL) algorithm with a multi-variable optimization approach has been used for the AD approach. The multi-variable optimization technique has been used to set the hyperparameters. The Ethereum framework creates privacy-focused BC and smart contract techniques that safeguard decentralized AD engines. The proposed M-LSTM model has the highest detection rate of 99.1%. The findings show the effectiveness of the proposed systems in identifying assaults on IoT networks.
Morena Barboni, Andrea Morichetta, Andrea Polini, Sebastian Bănescu · 5 authors
No abstract is available for this record.
Dahong Qian, Yiyang Xu, Yuncong Hu
No abstract is available for this record.
Nima Modrek Alsaiary, Shakeel Ahmed
The present era is witnessing rapid development in the smartphone revolution, where mobile users can access many required services through mobile applications. These services include healthcare, finance, learning, insurance, and the Internet of Things. Android has become the most popular smartphone operating system, and this rapidly increasing adoption of Android has led to a significant increase in the number of malware compared to previous years, resulting in issues related to the insecurity of mobile applications and the violation of their users' privacy. A large role in this mobile phone revolution has been played by the emergence of blockchain technology (BCT), which represents an effective way to develop applications, improve their security, and protect the privacy of their users. The aim of this paper is to design a system architecture for the software and provide a prototype that uses BCT at its core for the purpose of storing and sharing malicious data while also integrating a malicious detection technique. We employed the design science research approach to guide our project. Through this approach, we successfully integrated a malware detection technique with BCT in an Android application, resulting in a decentralized feature that enables peers to add and share malware data. Furthermore, we have developed an easy-to-use interface that allows users to interact with the blockchain in the Android application. This interface displays the results of the malware signature detection algorithm and enables users to take appropriate action if malware is detected. We have employed a design science methodology to develop a software framework, and open-source code meeting the tool's requirements has been provided. Finally, the proposed methodology has been evaluated against the existing methodologies and compared with the other techniques to demonstrate the effectiveness of the blockchain-based detection tools.
A. Venkata Nagarjun, R. Sujatha
Cloud-based deployments face increasing threats from various types of attacks, necessitating robust anomaly detection frameworks to safeguard against potential security breaches. Existing solutions, such as RSSI, GTM, and APG, though effective to a certain extent, exhibit limitations in terms of precision, accuracy, and scalability. To address these shortcomings, this paper proposes a novel anomaly detection framework that integrates multimodal feature analysis, deep learning models, and QoS-aware sidechains to enhance the prediction accuracy of cloud attacks and optimize blockchain-based cloud installations. By maximizing feature variance across different sample types and leveraging advanced deep learning techniques, the proposed approach significantly outperforms conventional methods in terms of precision, accuracy, recall, and AUC performance. Furthermore, the framework demonstrates superior efficiency in block mining delay, energy consumption, and throughput, making it highly suitable for real-time cloud attack prediction scenarios. The proposed methodology represents a significant advancement in anomaly detection and cloud security, offering a comprehensive solution for addressing challenges in blockchain-based cloud deployments. Thus, the proposed anomaly detection framework employs both Deep Learning and Blockchain technologies. Using Recurrent Neural Networks (RNN) with Convolutional Neural Networks (CNN), the system examines system logs and identifies unusual behavior patterns associated with different attacks. Using Blockchain technology, the framework ensures the transparency and integrity of system logs, and Deep Learning models provide precise and timely anomaly detection. The decision to combine Deep Learning and Blockchain technology is justified by the merits of each technique. The distributed, immutable ledger provided by blockchain technology makes it impossible to tamper with system logs and ensures the accuracy of anomaly detection. While, deep learning models, have exceptional pattern recognition abilities and can adapt to changing attack methods, resulting in high precision, accuracy, recall, and AUC metrics. Analyses of experimental data demonstrate that the proposed framework is effective. The framework achieves impressive performance metrics, such as low delays, 98.5% precision, 99.4% accuracy, 98.3% recall, and 99.2% Area Under the Curve (AUC).
Francisco Moya, Francisco J. Quesada, Luis Martı́nez, Francisco J. Estrella
No abstract is available for this record.
Shahriar Ebrahimi, Parisa Hassanizadeh
Remote attestation (RA) protocols have been widely used to evaluate the integrity of software on remote devices.Currently, the state-of-the-art RA protocols lack a crucial feature: transparency.This means that the details of the final attestation verification are not openly accessible or verifiable by the public.Furthermore, the interactivity of these protocols often limits attestation to trusted parties who possess privileged access to confidential device data, such as pre-shared keys and initial measurements.These constraints impede the widespread adoption of these protocols in various applications.In this paper, we introduce zRA, a non-interactive, transparent, and publicly provable RA protocol based on zkSNARKs.zRA enables verification of device attestations without the need for pre-shared keys or access to confidential data, ensuring a trustless and open attestation process.This eliminates the reliance on online services or secure storage on the verifier side.Moreover, zRA does not impose any additional security assumptions beyond the fundamental cryptographic schemes and the essential trust anchor components on the prover side (i.e., ROM and MPU).The zero-knowledge attestation proofs generated by devices have constant size regardless of the network complexity and number of attestations.Moreover, these proofs do not reveal sensitive information regarding internal states of the device, allowing verification by anyone in a public and auditable manner.We conduct an extensive security analysis and demonstrate scalability of zRA compared to prior work.Our analysis suggests that zRA excels especially in peer-to-peer and Pub/Sub network structures.To validate the practicality, we implement an open-source prototype of zRA using the Circom language.We show that zRA can be securely deployed on public permissionless blockchains, serving as an archival platform for attestation data to achieve resilience against DoS attacks.
Ghassan Samara, Abeer Al-Mohtaseb, Hayel Khafajeh, Raed Alazaidah · 8 authors
Cryptocurrencies are crucial in modern commerce and finance, whether at the national, corporate, or individual level. They serve as fundamental currencies for buying and selling, enabling various business transactions. However, the rise of cybercrime has brought about concerns regarding their operations, potential breaches in encrypted currencies, and the security systems managing them. The frequency of attack tactics and the motivation of attackers seeking financial gain are well-known. Many cryptocurrencies lack the necessary algorithms, techniques, and knowledge to effectively detect and mitigate malware, making them vulnerable targets for hackers. In this study, machine learning techniques are employed to detect malicious code in digital currencies. Additionally, a comparison of these techniques is conducted to determine the most suitable algorithm and technology, Furthermore, this study highlights the importance of effective malware detection in securing cryptocurrencies. Three datasets of different sizes were used, each yielding distinct results based on dataset size. The AdaBoost model demonstrated superior performance when applied to the short dataset, while the decision tree model performed best with the medium-sized dataset. Conversely, the Naive Bayes model consistently produced the worst results, while the large-size KNN model achieved the highest performance.
Saloni Jain, Ashwija Reddy Korenda, Amisha Bagri, Bertrand Cambou · 5 authors
No abstract is available for this record.
Dipayan Chakraborty, Sangita Mazumder, Ashutosh Kar
No abstract is available for this record.
Ashwija Reddy Korenda, Saloni Jain, Bertrand Cambou
No abstract is available for this record.
Yongming Fan, Yuquan Xu, Christina Garman
No abstract is available for this record.
Geethanjali Somasundaram
The advent of Blockchain and its subsequent application in creating Bitcoin has changed the world of finance. The peer-to-peer Blockchain networks, lack a third-party intermediary authority to regulate the transactions, making it vulnerable to various forms of stings. One of the most proliferate uses of crypto transactions is for the ransom payment made by victims of ransomware attacks. Owing to the varied nature of the ransomware attacks, coupled with the decentralized nature of Blockchain, tracking and guarding against such attacks is still a challenge. One way to prevent ransomware attackers from easily benefitting from such crypto transactions is to identify them and avert any payment to those attackers. In this paper, the impact of three ensemble classification algorithms – Random Forest, XGBoost and Balanced Bagging are studied to correctly classify ransomware payments from existing Bitcoin transaction data, to identify the attackers’ addresses and possibly suspend them from taking part in any transactions. The outcomes of the three algorithms are compared with each other based on various indicators. From the experimental results, it could be concluded that Balanced Bagging Classifier demonstrated better performance with an accuracy of 98.41%.
Vincent Jacquot, Benoît Donnet
No abstract is available for this record.
Bahareh Parhizkari, Antonio Ken Iannillo, Christof Ferreira Torres, Sebastian Bănescu · 6 authors
No abstract is available for this record.
Ho-Won Lee, Yoon-Young Park, Sungchul Lee, Yoon-Jae Chae
In today’s rapidly evolving digital landscape, ensuring data integrity is paramount for maintaining the security and reliability of applications. This paper introduces the Application Integrity Assurance System (AIAS), a novel solution designed to enhance data integrity through the integration of Ethereum blockchain technology. AIAS leverages smart contracts and the Interplanetary File System (IPFS) to securely store and verify application manifests. By decentralizing the integrity assurance process, AIAS mitigates the risks associated with tampering and unauthorized modifications, providing a robust framework for maintaining data integrity in various application environments. The system has been prototyped and tested on an augmented reality platform, demonstrating its practical application and efficiency. The AIAS framework offers a cost-effective, infrastructure-free solution for safeguarding application integrity, making it an essential tool for platforms that demand high standards of data integrity and security.
Takayuki Sasaki, Jia Wang, Kazumasa Omote, Katsunari Yoshioka · 5 authors
In recent years, Ethereum, which is a leading application for realizing blockchain services, has received much attention for its usability and functionality. Ethereum executes smart contracts and arbitrary programmable calculations, in addition to cryptocurrency trading. However, cyberattacks target misconfigured Ethereum clients with application programming interface (API) enabled, specifically JSON-RPC. Herein, we propose EtherWatch, a framework to detect and analyze malicious and/or suspicious Ethereum accounts using three data sources (a honeypot, an internet-wide scanner, and a blockchain explorer). The honeypot, named Etherpot, leverages a proxy server placed between a real Ethereum client and the internet. It modifies client responses to attract attackers, identifies malicious accounts, and analyzes their behaviors. Using scan results from Shodan, we also detect suspicious Ethereum accounts registered on multiple nodes. Finally, we utilize Etherscan, a well-known blockchain explorer, to track and analyze the activities of the detected accounts. During six weeks of observations, we discovered 538 hosts attempting to call JSON-RPC of our honeypots using 41 types of methods, including a type of unreported attack in the wild. Specifically, we observed account hijacking, mining, and smart contract attacks. We detected 16 malicious accounts using the honeypots and 64 suspicious accounts from the Shodan scan results, with five overlapping accounts. Finally, from Etherscan, we collected records of activities related to the detected accounts, including transactions of 21.50 ETH and mining of 22.61 ETH (equivalent to 39,494 US$ and 41,533 US$, respectively, as of June 9, 2023).
Alpesh Bhudia, Daniel O’Keeffe, Darren Hurley-Smith
No abstract is available for this record.