Blockchain Papers

Zhe Chen, Jia Huang, ShengZheng Liu, Haixia Long

No abstract is available for this record.

Koya Haritha, Sai Srinivas Vellela, D Roja, Lakshma Reddy Vuyyuru · 6 authors

Blockchain and SDN are combined in the "DistB-SD Cloud" architecture to secure and execute cloud-integrated IoT data. Cloud Computing Management and Services, Distributed Secure Blockchain Methodology, SDN Environment, and Data Extraction comprise the architecture. SDN-enabled devices safely gather and process sensor data in the data plane, while OpenFlow and OpenStack handle network component communication in the control plane. The application layer improves network administration with dynamic configuration and data analytics. Distributed nodes and hash chains ensure system integrity in blockchain-based access control and ledger maintenance. Blockchain and SDN improve security, privacy, and scalability by resisting attacks. The architecture outperforms OpenFlow-based SDN models in throughput and bandwidth, proving its stability under different network conditions. The study shows that the suggested "DistB-SD Cloud" achieves increased throughput (22.1 KB/s) and bandwidth (1.9 GB/s) when nodes and packet arrival rates increase, making it a feasible solution for safe and scalable cloud computing in IoT-based systems.

ES Phalguna Krishna, Daria Sandeep, Raviteja Kocherla, Kranthi Kumar Lella · 7 authors

No abstract is available for this record.

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.

Maxim Kalinin, Alexey Busygin, A. S. Konoplev, Vasiliy Krundyshev

Abstract— This article discusses the ways of using distributed ledger technology to ensure the security of smart city information systems. The authors outline the limitations of the existing solutions in this area and the main directions of development of distributed ledger technology, determining its successful integration into the smart city ecosystem.

Rahul Reddy, Pushpinder Singh Pateja, Adarsh Patel, Gopinath Palaniappan · 5 authors

Blockchain technology is significant because it makes data sharing between several parties safe, transparent, and effective. Multi-step transactions that require verification and traceability can benefit from Blockchain technology. However, the Blockchain technology too comes with its own vulnerabilities and often Blockchain networks are attacked by attacks like 51% Attack, Eclipse Attack, Sybil Attack, Time jacking Attack, Selfish Mining Attack, Finney Attack, Race Attack and so on. One among those attacks is the Sybil attacks, which are a big threat to the integrity of Blockchain networks since they assist malicious actors to create several identities, potentially overwhelming the system and defeating the very principle of consensus mechanisms. In this paper, we have captured an approach on designing a multi-layered mechanism for identifying Sybil attacks with the integration of behavioral analysis, Blockchain analysis techniques, Zero-Knowledge Proofs (ZKPs), and a robust security architecture for governance and validator selection. The broad idea is to cancel pseudo-anonymity in the Blockchain systems by detecting behavioral patterns, identifying exchange wallets, and mapping inter-wallet relationships. Integration of these approaches with ZKPs assists in improving identity verification while simultaneously maintaining user anonymity. The proposed architecture for security uses community-based governance and adaptive validator selection processes to strengthen the defense against Sybil attacks. Token concentration analysis traces down the distribution of stakes within the network in order to find potential risks due to centralization. Our findings thus conclude that integration of the security framework along with behavior analysis and ZKPs effectively reduces the proliferation of fraudulent identities in the Blockchain networks.

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.

Luan Van-Thien, Duc Vu-Minh, My Duong-Tran-Tra, Anh Pham-Nguyen-Hai · 7 authors

In recent years, IoT applications have become increasingly popular. Smart services have been deployed from the IoT infrastructure to provide convenience for humans in their lives and related activities. Alongside IoT’s potential, security and privacy concerns have been highlighted in the IoT architecture. One weakness of the IoT system is the widespread deployment of sensor nodes with wireless connections. Additionally, the limited resources of these sensor nodes pose a challenge in designing and implementing security solutions for the IoT infrastructure. In this article, we plan to deploy a Network Intrusion Detection System (NIDS) for IoT infrastructure. This system is designed to run on the Swarm Learning framework, which supports decentralized machine learning models to ensure data distribution during training. This framework also operates on Ethereum – an open-source blockchain platform - to ensure authenticity and security while training decentralized machine learning models. We experiment with various scenarios using the CNN model via the CICIoT2023 dataset. The results demonstrate that our proposed system ensures accuracy comparable to centralized machine learning and Federated Learning models. We also experiment and evaluate the merge methods of decentralized machine learning models. Even though the mean method achieves the best performance, the coordmedian and geomedian methods give greater accuracy in results.

Duc Vu-Minh, My Duong-Tran-Tra, Luan Van-Thien, Anh Pham-Nguyen-Hai · 6 authors

In recent years, IoT applications have become increasingly popular. Smart services have been deployed from the IoT infrastructure to provide convenience for humans in their lives and related activities. Alongside IoT’s potential, security and privacy concerns have been highlighted in the IoT architecture. One weakness of the IoT system is the widespread deployment of sensor nodes with wireless connections. Additionally, the limited resources of these sensor nodes pose a challenge in designing and implementing security solutions for the IoT infrastructure. In this article, we plan to deploy a Network Intrusion Detection System (NIDS) for IoT infrastructure. This system is towards to runs on the Swarm Learning framework, which supports decentralized machine learning models to ensure data distribution during training. This framework also operates on Ethereum – an open-source blockchain platform - to ensure authenticity and security while training decentralized machine learning models. We experiment with various scenarios using the DNN model via the CiCIoT2023 dataset and the CiCIoMT24 dataset. The results demonstrate that our proposed system ensures accuracy comparable to centralized machine learning and Federated Learning models. In addition, we also tested and evaluated based on training time and resource usage, thereby concluding that the cost and effectiveness of the Swarm Learning system is better than Federated Learning.

Ernest Ntizikira, Lei Wang, Jenhui Chen, Kiran Saleem

No abstract is available for this record.

Mikhail Dymkov, Vladimir Gorgadze, Alexey Karanyuk, Artem Barger

This paper establishes a groundbreaking framework for pinpointing and scrutinizing web3 protocols that display attributes akin to Ponzi schemes. We meticulously define the defining features of these protocols and introduce sophisticated methodologies to assess their stability, fine-tuning their parameters, and crafting economic mechanisms to boost their sustainability. The robustness of our framework is vividly showcased through comprehensive case studies of two prominent web3 protocols: Safemoon and Ethena. In the Ethena case study, we take a step further by devising an advanced economic mechanism for automatic interest rate regulation, employing an innovative feedback loop system.

Stefano Tempesta

Web3 applications, which are built on blockchain and decentralized technologies, introduce a unique set of security threats compared to traditional web applications. In this chapter we are going to look at some common security threats for Web3 applications and best practices to mitigate these risks.

Priyanka Pawar, Deepak Kumar, Mohan Kumar Meesala, Piyush Kumar Pareek · 6 authors

This research uses deep learning and blockchain frameworks to provide a safe platform that promotes digital governance data exchange and interoperability. Use the bonobo optimization algorithm to start a blockchain-based smart city data authentication approach. This paper presents a Blockchain-based malware detection method and framework that uses AI to account for multiple distributed conditions. An upgraded greedy search algorithm and the XGBoost decision tree construct a two-layer extreme gradient boosting (XGBoost) classification model that detects attacks. Three pre-existing XGBoost significance indices were split and merged based on the model's leaf nodes' tree traversal structural features. The augmented greedy search technique retrieved and imported spectral band variables into the XGBoost model's second layer. Bat method was used to optimize XGBoost modeling parameters. The deployed model increased power consumption per device by 13.5%, while Raspberry Pi devices used 0.2 GB and NVIDIA Jetson devices used 0.42 GB. ML models had 93% f1-scores and 95% detection accuracy on both datasets. Our technology detects malware and attacks in Smart Environments efficiently and accurately, as shown by the models.

Mehmed Kerem Uludag, Murat Karakuş, Evrim Güler, Suleyman Uludag

Software-Defined Networking (SDN) and Blockchain (BC) are transformative technologies reshaping network management and security, utilizing their synergies. SDN’s centralized control enhances flexibility and efficiency but introduces vulnerabilities due to its single point of control. With its decentralized and immutable ledger, BC offers a solution by distributing control and providing a tamper-proof audit trail. This paper integrates these technologies to address blackhole attacks—a critical vulnerability where compromised SDN controllers disrupt network performance. We propose Blockchain-Enhanced SDN for Adaptive Path Finding (BeS4APF) algorithm against blackhole attacks in the multidomain SDNs. The algorithm maintains a high Packet Delivery Ratio (PDR) by dynamically adjusting network paths in response to node failures. The BeS4APF algorithm presented effectively maintains a high PDR by dynamically adjusting paths in response to node failures. The methodology involves monitoring the network, detecting compromised nodes, and recalculating optimal paths using Dijkstra’s algorithm and node-disjoint path selection. Experiments with synthetic networks of varying sizes (from 60 to 120 domains) demonstrate that the algorithm successfully handles domain-compromising node attacks, maintaining high PDR even with increasing stochastic disruptions. Our preliminary results show that, while PDR slightly deviates and recovers in smaller networks, it stabilizes towards almost 100% in larger networks after initial adjustments. This work advances the integration of SDN and BC, offering a robust approach to securing modern networks against evolving stochastic attack scenarios and threats.

Sonali Kothari, Tejas Ulbhaje, Vishant Kalwani, Yash Mangale · 5 authors

In this research paper, we introduce a Kotlin application that assesses different security features on the Android SDK and executes them. The objectives are:1.Improving security of mobile phones—The most commonly used for m-payments; High-value targets, because they store substantial amount of personal information (especially financial details);2.Some of the available features include data obfuscation and anti-screenshot which is meant to be obedient with security guidelines set by RBI for financial apps.3.Zero-Knowledge Proof — enables the parties to verify facts about each other without sharing personal data and removes risks associated with unauthorized access or data breaches, significantly enhancing security for end-users.It is important to have this kind of initiative for any financial application that you develop, otherwise how can an app user trust your code and be sure it met all possible regulations?!

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.

Sunil Raj Y, E. Helen Parimala, V. S Jayakumar Paul Bosco, Aarav Kannan J · 6 authors

In the growing field of the Internet of Things (IoT), ensuring security and privacy has become a critical concern, particularly in sensitive domains such as healthcare. This paper presents a Real-Time Adaptive Intrusion Detection System (IDS) specifically designed for Internet of Medical Things (IoMT) networks. The proposed system leverages Federated Learning (FL) and Blockchain technologies to address key challenges, including handling non-independent and identically distributed (non-IID) data, ensuring secure model updates, and providing real-time detection capabilities. By employing FL, the system enables IoMT devices to collaboratively train models without sharing sensitive data, thereby preserving privacy and improving scalability. Blockchain integration further enhances the system by ensuring the integrity and security of model updates, mitigating risks of tampering. The system demonstrates strong performance in detecting simpler intrusions in real-time, though additional enhancements are required to manage more complex attacks and large-scale IoT deployments. This work contributes a robust and scalable IDS framework, particularly valuable for protecting critical IoT environments such as healthcare networks.

Vikas Sharma, Akshi Kumar, Kapil Sharma

No abstract is available for this record.

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.

Jianqi Wei, Yuling Chen, Yun Luo, Zai-Dong Li

With the rapid development of Internet of Things (loT) technology, the vast amount of data generated by its devices has raised widespread concern for user privacy pro-tection. Differential Privacy, as a stringent privacy protection measure, plays a significant role in safeguarding individual data. However, implementing Differential Privacy in the loT environment faces challenges in ensuring data utility. This paper focuses on proposing a Verifiable Differential Privacy (VDP) scheme based on zero-knowledge proofs, under the premise of ensuring data utility. The scheme requires data publishing entities to provide publicly verifiable proofs to confirm the reliability of the dataset and the effectiveness of privacy protection. By introducing a commitment mechanism and range proofs, our model not only protects the interests of data users but also enhances trust in the enforcement of privacy protection measures. The experimental results show that the scheme can effectively filter out unreasonable Differential Privacy noise, ensuring the privacy and reliability of data, offering a new approach to data privacy protection in the loT field.

Chao Li, Xiangkai Zhu, Jianjia Li, Nengfu Xie · 5 authors

No abstract is available for this record.

Mimouna Abdullah Alkhonaini, Manal Abdullah Alohali, Mohammed Aljebreen, Majdy M. Eltahir · 8 authors

Intrusion detection in the Internet of Things (IoTs) is a vital unit of IoT safety. IoT devices face diverse kinds of attacks, and intrusion detection systems (IDSs) play a significant role in detecting and responding to these threats. A typical IDS solution can be utilized from the IoT networks for monitoring traffic, device behaviour, and system logs for signs of intrusion or abnormal movement. Deep learning (DL) approaches are exposed to promise in enhancing the accuracy and effectiveness of IDS for IoT devices. Blockchain (BC) aided intrusion detection from IoT platforms provides many benefits, including better data integrity, transparency, and resistance to tampering. This paper projects a novel sandpiper optimizer with hybrid deep learning-based intrusion detection (SPOHDL-ID) from the BC-assisted IoT platform. The key contribution of the SPOHDL-ID model is to accomplish security via the intrusion detection and classification process from the IoT platform. In this case, the BC technology can be used for a secure data-sharing process. In the presented SPOHDL-ID technique, the selection of features from the network traffic data takes place using the SPO model. Besides, the SPOHDL-ID technique employs the HDL model for intrusion detection, which involves the design of a convolutional neural network with a stacked autoencoder (CNN-SAE) model. The beetle search optimizer algorithm (BSOA) method is used for the hyperparameter tuning procedure to increase the recognition outcomes of the CNN-SAE technique. An extensive simulation outcome is created to exhibit a better solution to the SPOHDL-ID method. The experimental validation of the SPOHDL-ID method portrayed a superior accuracy value of 99.59 % and 99.54 % over recent techniques under the ToN-IoT and CICIDS-2017 datasets.

Cong Huang, Liyong Nong, Yingxiong Nong, Ying Lu · 6 authors

This paper studies the detection model of network access data tampering attack based on blockchain technology to solve the problem of over-dependence on central server and easy data tampering in traditional network environment. The model uses decentralization and encryption technology to monitor user behavior in real time through smart contracts, enhances data protection with SHA-256 hash algorithm, and combines consensus algorithm to ensure data consistency and security. The experimental results show that the model performs well in detecting multiple attack types with an accuracy of 99.51% and an F1 score of 0.98, far exceeding traditional methods and other deep learning techniques. The model shows good robustness under multi-node attacks, even with 200 attack nodes, the recognition accuracy is still close to 90%, and the response time is less than 3 seconds. Cross-platform testing showed that the model quickly and consistently detected tampering on both Ethereum and Hyperledger, with an average detection time between 0.33 and 0.47 seconds.The hardware acceleration test further shows that the processing speed and hardware utilization of TPU and GPU have been improved, with TPU processing speed reaching 135 MB/s and GPU 122 MB/s. This study will provide a theoretical basis for improving the security, effectiveness and reliability of current network systems, and also lay a solid theoretical and technical foundation for network applications in future network environments.

Nujud AlHajri

This paper is intended to elaborate on the 4th Industrial Revolution (Industry 4.0) that will alter and shift technologies and the utilities sector. Alongside advanced Cybersecurity technologies to be aligned with the need for more resilience and secured environments that empower digital transformation. Where we explore the drivers and applications of Cybersecurity considering 4thIR as an engine of digital transformation, that is governed and measured by complying with digital transformation rules, flowing from modern technology to the evolution where the quality of life is measured by its speed, energy, and security. As such, Industrial Control Systems (ICS) are a solid infrastructure for such a digital transformation. As the acceleration of cyberattacks are becoming more sophisticated, a well Cybersecurity strategies must be applied. Furthermore, we propose advanced Cybersecurity solutions for the smart grid that can be addressed by the efficiency of the Next Generation (SOC) and the utilization of Distributed Ledger Technology (DLT).