Blockchain Papers

Mohamed Fartitchou, Ismail Lamaakal, Yassine Maleh, Khalid El Makkaoui · 8 authors

Software-Defined Networking (SDN) has revolutionized network management by providing unprecedented flexibility, control, and efficiency. However, its centralized architecture introduces critical security vulnerabilities. This paper introduces a novel approach to securing SDN environments using IOTA 2.0 smart contracts. The proposed system utilizes the IOTA Tangle, a directed acyclic graph (DAG) structure, to improve scalability and efficiency while eliminating transaction fees and reducing energy consumption. We introduce three smart contracts: Authority, Access Control, and DoS Detector, to ensure trusted and secure network operations, prevent unauthorized access, maintain the integrity of control data, and mitigate denial-of-service attacks. Through comprehensive simulations using Mininet and the ShimmerEVM IOTA Test Network, we demonstrate the efficacy of our approach in enhancing SDN security. Our findings highlight the potential of IOTA 2.0 smart contracts to provide a robust, decentralized solution for securing SDN environments, paving the way for the further integration of blockchain technologies in network management.

Javier Ramı́rez, D. Jonathan Sebastian-Cardenas

Smart grid technologies have rapidly become one of the largest and most comprehensive sources of data for the modern utility. For the most part, data streams are seen as an essential tool that enable utilities to carry their day-to-day business operations, but they also create the need for efficient and secure data management strategies. In the context of the smart grid, ensuring data privacy is becoming an increasing concern due to a combination of factors that range from shifts in operational paradigms and rapid technology evolution to changes in legislation. Furthermore, researchers have highlighted the risks associated with improperly protected energy records. For example, energy consumption data from homes could be used to infer the behaviors and habits of home occupants through activity recognition or user profiling (Fan, 2017), which may lead to unfair service pricing, targeted advertising, or other personal security violations. Similarly, Electric Vehicles’ (EVs) charging metadata could be used to reveal private information about the owner such as their payment methods, preferred charging stations, and other locational and timing information that could be used to reconstruct the vehicle owner’s behaviors. The privacy of user data, even when used for statistical analysis or machine learning training processes, also needs to be carefully considered, as an individual’s private traits may still be vulnerable if their inclusion/exclusion greatly impacts the result or could be linked to a public dataset through cross-reference. The breach of user privacy also has severe impacts for organizations that store, transmit, or work on the data in the form of diminishing the public’s trust in them while potentially incurring legal consequences (e.g., fines and suspensions under the European Union General Data Protection Regulation, Health Insurance Portability and Accountability Act, etc.). Because of these risks, several privacy-preserving mechanisms are available to help organizations comply with privacy legislations and prevent the unauthorized and malicious use of user data. In light of these concerns, this report focuses on performing a computational review of privacy-preserving mechanisms that have received a significant amount of interest in literature. It specifically focuses on 1) homomorphic encryption, 2) zero-knowledge proofs, 3) differential privacy, and 4) federated learning. It is worth noting that although many of the methods presented in this document rely on cryptographic primitives, their intent is not to provide perfect secrecy, but rather to enable users to maintain privacy, and thus they shall not be compared or equated to other constructs that are aimed to address cybersecurity constructs.

Andrew R. Short, Τheofanis G Orfanoudakis, Helen C. Leligou

This research paper discusses security issues with current deployments of Programmable Logic Controllers (PLCs) and Supervisory Control and Data Acquisition Systems (SCADA) in the industry and proposes a solution that enables PLC devices to query a blockchain infrastructure for commands and setpoints. The blockchain assumes a dual role in this context: serving as an immutable audit trail database as well as a trusted source for critical commands and setpoints. In contrast to the conventional paradigm, this novel approach does not require write access at the PLC level, thus minimizes its attack surface and helping to protect against known and zero-day vulnerabilities often used in cyberwarfare, such as in the case of the notorious Stuxnet worm. Applications that enforce the logging of user operations for Good Manufacturing Practices (GMP) or compliance purposes use the blockchain network as an audit trail database for user actions. Any attempt to maliciously circumvent the logging operation would not affect the operation of a critical process. Additionally, a prototype implementation developed as part of this research finds that modern PLC devices are more than capable of interacting with private Ethereum blockchain nodes. The required libraries and user code consume a small percentage of available resources, while the duration of a complete request-response cycle measured around 22msec. The authors anticipate that PLCBlox can be used as a drop-in replacement for applications requiring higher security standards and logging enforcement, such as nuclear power plants or other critical infrastructure.

Gary Hahn, Emilio C. Piesciorovsky, Raymond Borges Hink, Aaron Werth

• Faulted phases detection using backup method that is external to protective relay. • Validates integrity of input data using distributed ledger technology. • Tested using simulated faults in a realistic electrical substation test bed. Modern electrical grids have intelligent electronic devices (IEDs) such as protective relays that use internal logic to detect the types of electrical faults. The increasing integration of distributed energy sources and the resulting complexity of electrical grid communication architectures necessitates enhanced robustness of IEDs’ monitoring while maintaining security against potential cyber threats. In this study, a backup electrical faulted phase detection method with a distributed ledger technology (DLT) platform was implemented. Cyber Grid Guard software was developed to collect phase currents and voltages transmitted through IEC 61850 GOOSE messages, detect faulted phases from the IEDs using the GOOSE data, and validate the data by hashing them and storing them in the distributed ledger. In this way, the hashed data were run into an electrical faulted phase algorithm based on using a current threshold for detecting the faulted phases in the medium-voltage main feeder of an electrical substation. The detection of the electrical faulted phases was assessed in a real-time simulator with protective relays, meters, the software framework, and DLT in the loop. The proposed method provides secure and reliable backup detection external to the IEDs, and DLT validation enhances system security and trust.

Khadija Begum, Md Ariful Islam Mozumder, Moon-Il Joo, Hee‐Cheol Kim

The Internet of Medical Things (IoMT) has significantly advanced healthcare, but it has also brought about critical security challenges. Traditional security solutions struggle to keep pace with the dynamic and interconnected nature of IoMT systems. Machine learning (ML)-based Intrusion Detection Systems (IDS) have been increasingly adopted to counter cyberattacks, but centralized ML approaches pose privacy risks due to the single points of failure (SPoFs). Federated Learning (FL) emerges as a promising solution, enabling model updates directly on end devices without sharing private data with a central server. This study introduces the BFLIDS, a Blockchain-empowered Federated Learning-based IDS designed to enhance security and intrusion detection in IoMT networks. Our approach leverages blockchain to secure transaction records, FL to maintain data privacy by training models locally, IPFS for decentralized storage, and MongoDB for efficient data management. Ethereum smart contracts (SCs) oversee and secure all interactions and transactions within the system. We modified the FedAvg algorithm with the Kullback-Leibler divergence estimation and adaptive weight calculation to boost model accuracy and robustness against adversarial attacks. For classification, we implemented an Adaptive Max Pooling-based Convolutional Neural Network (CNN) and a modified Bidirectional Long Short-Term Memory (BiLSTM) with attention and residual connections on Edge-IIoTSet and TON-IoT datasets. We achieved accuracies of 97.43% (for CNNs and Edge-IIoTSet), 96.02% (for BiLSTM and Edge-IIoTSet), 98.21% (for CNNs and TON-IoT), and 97.42% (for BiLSTM and TON-IoT) in FL scenarios, which are competitive with centralized methods. The proposed BFLIDS effectively detects intrusions, enhancing the security and privacy of IoMT networks.

Wanning Ding, Yuzhe Tang, Yibo Wang

A mempool is a security-critical subsystem in a public blockchain. Recent mempool attacks, notably asymmetric DoS, have shown their ability to severely damage the Ethereum network. This paper tackles the open research problem of designing principled and non-intrusive defenses against asymmetric mempool DoSes with provable security. It presents the first mempool economic-security definitions based on mempool-observable conditions. It then presents SAFERAD, a framework of secure mempool designs with provable security against asymmetric DoSes. To defend against dual attacks by evicting and locking a victim mempool, SAFERAD adopts a non-trivial design of enforcing an upper bound of the attack damage under the locking attacks and a lower bound of the attack cost under the eviction attacks. With a prototype implementation on Geth and evaluation under real transaction traces, the results show SAFERAD has low overhead in latency and block revenue, implying non-intrusiveness and practicality.

Muhammad Faheem, Basit Raza, Muhammad Shoaib Bhutta, Syed Hamid Hussain Madni

Abstract The rapid and green energy transition is essential to deal with the fast‐growing energy needs in both public and industrial sectors. This has paved the way to integrate distributed renewable energy resources () such as solar, hydro, wind, and geothermal into the power grid (). Wind and solar are free, zero‐carbon emission, and everlasting power sources that contribute 5% and 7% of global electricity generation, respectively. Therefore, the fast, secure, and reliable integration of these green is critical to achieve the instant energy demands. Smart grid due to inherited characteristics such as intelligent sensing, computing, and communication technologies can effectively integrate the . However, the existing smart grid communication architecture faces various cyberattacks, resulting in poor integration, monitoring, and control of . In this respect, blockchain technology can provide fast, secure, and efficient end‐to‐end communication between in the smart grid. In this study, the authors propose a blockchain‐based resilient and secure scheme called for wireless sensor networks ‐based events monitoring and control in . Experimental studies and performance analyses are carried out to predict the efficiency of the proposed scheme by considering numerous standard metrics. The extensive numerical results demonstrated that the proposed scheme is significant in terms of secure, resilient, and reliable information transmission for in .

Seyed Hossein Rouhani, Chun‐Lien Su, Saleh Mobayen, Mostafa Esmaeili Shayan · 6 authors

The advancement and proliferation of digitalization and communication infrastructure have facilitated the rise of real-time bidding markets in smart grids. In these dynamic markets, energy distribution companies and power-generating companies interact to establish energy exchange contracts based on offered prices. However, the fluctuation in power flow resulting from contract changes within the real-time bidding market introduces a potential vulnerability that malicious attackers can exploit to launch successful stealthy attacks. To enhance the smart grid resiliency against cyber-attack in the power market bidding environment, a new barrier-function adaptive finite-time trajectory tracking control is proposed in this paper. The developed controller is utilized to actively counteract and mitigate potential cyber-attacks to ensure their rejection and prevention. The stability analysis convincingly demonstrates the rapid convergence of system states within a finite time frame, empowering the system to effectively reject cyber-attacks in real-time. Test results of an IEEE test systems, considering governor dead bound nonlinearity and communication time delay are presented and compared with those obtained from other methods to ensure and demonstrate the performance of proposed method. The Speedgoat real-time target machine, along with Simulink real-time, validates the effectiveness of the proposed method.

S. B. Goyal, Anand Singh Rajawat, Ritu Shandilya, Varun Malik

Industrial Internet of Things (IIoT) solutions have transformed industrial productivity and operations. The incorporation of Ethereum blockchain technology into IIoT creates new weaknesses, exposing industrial systems to several cyberattacks. An unique IIoT framework mitigates Ethereum-based attacks in industrial applications to solve these vulnerabilities. This system uses supervised learning and quantum classifiers to detect and fix fraudulent Ethereum transaction patterns in real time. Our methodology has lower false positive rates and higher detection accuracy than conventional methods, according to first trials. This study shows that quantum computing and machine learning (ML) can improve the security of Ethereum-enabled IIoT devices in industry.

Abrar Omar Alkhamisi, Iyad Katib, Seyed M. Buhari

A Multi-Controller Software-Defined Network (MC-SDN) is a revolutionary concept comprising multiple controllers and switches separated using programmable features, enhancing network availability, management, scalability, and performance. The MC-SDN is a potential choice for managing large, heterogeneous, complex industrial networks. Despite the rich operational flexibility of MC-SDN, it is imperative to protect the network deployment with proper protection against potential vulnerabilities that lead to misuse and malicious activities on the MC-SDN structure. The security holes in the MC-SDN structure significantly impact network survivability and performance efficiency. Hence, detecting MC-SDN security attacks is crucial to improving network performance. Accordingly, this work intended to design blockchain-based controller security (BCS) that exploits the advantages of immutable and distributed ledger technology among multiple controllers and securely manages the controller communications against various attacks. Thereby, it enables the controllers to maintain consistent network view and accurate flow tables among themselves and also neglects the controller failure issues. Finally, the experimental results of the proposed BCS approach demonstrated superior performance under various scenarios, such as attack detection, number of attackers, number of controllers, and number of compromised controllers, by applying different performance metrics.

Themistoklis Sarantakos, Dimitrios Amaxilatis, Antonino Pagano, Domenico Garlisi · 9 authors

The integration of smart contracts within water distribution networks presents a transformative approach to addressing challenges in water management. In this context, we propose a pioneering tool aimed at streamlining the design and implementation of smart contracts tailored specifically to smart water distribution networks. This tool allows stakeholders to input essential parameters such as water sources, distribution points, consumption patterns, and contractual stipulations. Through the utilization of predefined templates and adaptable contract logic, the tool automates critical processes including water allocation, usage monitoring, and penalty imposition based on predefined criteria. Furthermore, seamless integration with blockchain technology ensures the security and integrity of contract execution. By addressing scalability, compliance, and regulatory considerations, this tool represents a significant advancement in empowering stakeholders to optimize water management practices through the deployment of efficient and transparent smart contracts.

Mayar Ibrahim Hasan Okfie, Shailendra Mishra

The integration of secure message authentication systems within the Industrial Internet of Things (IIoT) is paramount for safeguarding sensitive transactions. This paper introduces a Lightweight Blockchain-based Message Authentication System, utilizing k-means clustering and isolation forest machine learning techniques. With a focus on the Bitcoin Transaction Network (BTN) as a reference, this study aims to identify anomalies in IIoT transactions and achieve a high level of accuracy. The feature selection coupled with isolation forest achieved a remarkable accuracy of 92.90%. However, the trade-off between precision and recall highlights the ongoing challenge of minimizing false positives while capturing a broad spectrum of potential threats. The system successfully detected 429,713 anomalies, paving the way for deeper exploration into the characteristics of IIoT security threats. The study concludes with a discussion on the limitations and future directions, emphasizing the need for continuous refinement and adaptation to the dynamic landscape of IIoT transactions. The findings contribute to advancing the understanding of securing IIoT environments and provide a foundation for future research in enhancing anomaly detection mechanisms.

Precious Kgomotso Maine, Collins Achepsah Leke, Omowunmi Mary Longe

The Gautrain railway link in the Gauteng province of South Africa is a crucial transportation facility that has faced considerable energy-related difficulties and hence a dependable power supply is essential to ensure the continuous operation of railway processes. The total number of passenger trips on the Gautrain, in a year, is two million plus. The railway system, which has always relied on grid electricity to power trains, stations, lights, security, and operations is facing increasing difficulties because of rising electricity demand and costs, and power-related outages. To address these issues, the study designed an optimised power generation system for the Gautrain railway link (GRL) using a photovoltaic (PV) system with energy storage connected to the grid to lower energy costs, promote economic growth, and satisfy its energy demand. The Advanced Interactive Multidimensional Modelling System (AIMMS) tool was used to optimise the incorporation of solar energy generation and battery energy storage into the GRL power system. This study contributes to sustainable energy development by leveraging renewable solar energy, optimising the GRL power system, and integrating blockchain technology for peer-to-peer (P2P) energy trading. A smart contract is compiled and deployed within the Remix Online IDE to apply a logic that manages energy generation, consumption, and P2P energy trading amongst prosumers. It furnishes a replicable model for the advancement of electrical energy systems within diverse transportation networks, therefore fostering the adoption of energy-efficient practices.

Ameni Boumaiza

In the ever-changing global energy landscape, the emergence of ‘prosumers’, individuals who both produce and consume energy, has blurred traditional boundaries. Driven by the growing demand for sustainability and renewable energy, prosumers play a critical role in bridging the gap between energy production and consumption. They can generate their own energy through decentralized sources like solar panels and wind turbines, and sell excess energy back to the grid. However, tracking carbon emissions and pricing strategies for prosumers pose challenges. To address this, we developed an innovative blockchain-driven peer-to-peer (P2P) trading platform for carbon allowances. This platform empowers prosumers to influence pricing and promotes a more equitable distribution of energy. The P2P platform leverages blockchain technology, a decentralized digital ledger, to provide transparency and security in carbon emission tracking and energy transactions. By eliminating intermediaries, blockchain ensures the accuracy of data and creates a tamper-proof record of energy production and consumption. This study employed a modified IEEE 37-bus test system to evaluate the efficacy of the proposed blockchain-based trading framework. The IEEE 37-bus system is a well-established benchmark for power system analysis, comprising 37 nodes, 13 generators, and 37 transmission lines. By leveraging this test system, this study demonstrated the framework’s ability to optimize energy consumption patterns and mitigate carbon emissions, highlighting the transformative potential of blockchain technology in the energy sector. The proposed P2P trading platform offers several benefits for prosumers: (1) Transparency: The blockchain-based platform provides a transparent record of all energy transactions, ensuring that prosumers are compensated fairly for the energy they produce. (2) Security: Blockchain technology makes it impossible to tamper with or counterfeit carbon allowances, ensuring the integrity of the trading system. (3) Efficiency: The P2P trading platform eliminates the need for intermediaries, reducing the cost and complexity of energy transactions. (4) Empowerment: The platform gives prosumers a greater say in how their energy is priced and distributed, promoting a more equitable energy system.

Marisol García‐Valls, Alejandro M. Chirivella-Ciruelos

Non-functional requirements related to safety, security, and timeliness have made cyber–physical systems (CPS) initially reluctant to their integration with blockchain technology. Despite the multiple advantages of blockchain like improved data security and traceability, the main reasons that have slowed down its adoption in CPS still remain. Examples of these are the inherent overhead of accessing the distributed ledger and the security incidents that a number of blockchain networks have suffered since its inception. This paper presents VelogCPS, a novel middleware that guarantees that logic and data managed by blockchain networks of cyber–physical systems is verified and generated by a legitimate source. Thus, VelogCPS avoids a kind of security incidents that impact the authenticity and integrity of the logic and data managed in blockchain networks. By authenticity we refer to provenance authenticity of the involved smart contracts, i.e., the perfect matching between the source-code and a corresponding advertised smart-contract logic. This middleware ensures that the entities that participate to a CPS use solely authentic logic. For this, our approach leverages block verification services and enforces them through the operation workflow. As a result, the middleware guarantees that the CPS participants use and share authentic logic. Our approach is validated by providing an implementation on a real blockchain network, employing actual smart contract verifier logic, and analysing the temporal behavior of the overall system operations; this ensures its utility for CPS and IoT.

Emily Struble, Maikel León, Erotokritos Skordilis

(Distributed) Denial-of-Service (DoS/DDoS) attacks are among the most dangerous cybersecurity threats to computer networks. Lately, blockchain and artificial intelligence (AI) cyberdefense applications have successfully been implemented to identify attack patterns. This paper proposes a novel collaborative, blockchain-based multi-agent reinforcement learning (RL) cyberdefense method using smart contracts. Initial numerical experiments have shown that the agents quickly learn to predict attacks, which can lead to mitigating network-wide service disruptions.

Caixiang Fan, Hamzeh Khazaei, Petr Musı́lek

Recent years have witnessed a significant dispersion of renewable energy and the emergence of blockchain-enabled transactive energy systems. These systems facilitate direct energy trading among participants, cutting transmission losses, improving energy efficiency, and fostering renewable energy adoption. However, developing such a system is usually challenging and time-consuming due to the diversity of energy markets. The lack of a market-agnostic design hampers the widespread adoption of blockchain-based peer-to-peer energy trading globally. In this paper, we propose and develop a novel unified blockchain-based peer-to-peer energy trading framework, called BPET. This framework incorporates microservices and blockchain as the infrastructures and adopts a highly modular smart contract design so that developers can easily extend it by plugging in localized energy market rules and rapidly developing a customized blockchain-based peer-to-peer energy trading system. Additionally, we have developed the price formation mechanisms, e.g., the system marginal price calculation algorithm and the pool price calculation algorithm, to demonstrate the extensibility of the BPET framework. To validate the proposed solution, we have conducted a comprehensive case study using real trading data from the Alberta Electric System Operator. The experimental results confirm the system’s capability of processing energy trading transactions efficiently and effectively within the Alberta electricity wholesale market.

Muhammad Faheem, Mahmoud Ahmad Al‐Khasawneh

The world's need for energy is rising due to factors like population growth, economic expansion, and technological breakthroughs. However, there are major consequences when gas and coal are burnt to meet this surge in energy needs. Although these fossil fuels are still essential for meeting energy demands, their combustion releases a large amount of carbon dioxide and other pollutants into the atmosphere. This significantly jeopardizes community health in addition to exacerbating climate change, thus it is essential need to move swiftly to incorporate renewable energy sources by employing advanced information and communication technologies. However, this change brings up several security issues emphasizing the need for innovative cyber threats detection and prevention solutions. Consequently, this study presents bigdata sets obtained from the solar and wind powered distributed energy systems through the blockchain-based energy networks in the smart grid (SG). A hybrid machine learning (HML) model that combines both the Deep Learning (DL) and Long-Short-Term-Memory (LSTM) models characteristics is developed and applied to identify the unique patterns of Denial of Service (DoS) and Distributed Denial of Service (DDoS) cyberattacks in the power generation, transmission, and distribution processes. The presented big datasets are essential and significantly helps in identifying and classifying cyberattacks, leading to predicting the accurate energy systems behavior in the SG.

Ulysse Pavloff, Yackolley Amoussou-Guenou, Sara Tucci-Piergiovanni

International audience

Amin Zakhirehkar Sahih, Alireza Abbasi, Milad Ghasri

Peer-to-peer (P2P) renewable energy trading, facilitated by designing P2P market smart contracts on blockchain servers, is a promising approach to increase investments in cleaner energy generation. To enhance trading efficiency and pricing fairness, as the major challenges of P2P market designs, this study introduces two new market mechanisms, Hybrid Auction Coalition (HAC), and innovative coalition business model (ICBM), respectively. The performance of these market mechanisms is contrasted with existing mechanisms from the literature with respect to electricity bills, market efficiency, fairness, and blockchain feasibility using a comprehensive list of indicators including costs and profits, fairness, market efficiency, and technical viability. Compared to traditional billing, ICBM and HAC increase sellers’ profit by 88% and 66% respectively, while both impose 13% more costs on buyers. ICBM and HAC also set the fairest prices compared to the existing markets. ICBM is shown to improve blockchain feasibility and market efficiency due to lighter on-chain calculations, and absolute clearing mechanisms. The results also demonstrate that HAC outperforms standalone auctions in every aspect which endorses the hybridization benefits. • Novel and efficient P2P Models, and blockchain-based mechanisms for P2P renewable energy trading. • Enhanced fairness concerns in P2P trading through a novel pricing mechanism. • Improved P2P trading efficiency & potentially increased local grid self-sufficiency. • Technical comparison of state-of-the-art blockchain-based P2P trading markets.

Shuang Yao, Yayun Zhu, Xiaojuan Zhang, Dahua Zhang · 7 authors

Abstract As an important manifestation of the current development and transformation of the world’s power and energy industries, the virtual power plant is an important foundation for optimizing the layout of energy resources. However, since there are many open channels in the virtual power plant, adversaries can implement eavesdropping, replay, impersonation, forgery, and other attacks to access the virtual power plant, and even publish false data in the virtual power plant to disrupt the operation of the virtual power plant. In addition, it is easy for an adversary to deduce key information such as the layout of virtual power plant equipment through the identity of the device. In this context, to ensure the security and privacy of devices when accessing the platform, in this paper, we propose an efficient authentication protocol based on the elliptic curve cryptography and zero-knowledge proof, which requires only two information exchanges. Security analysis shows that the proposed protocol can meet security features such as mutual authentication, key agreement, perfect forward secrecy, and device anonymity. Performance analysis indicates that the proposed protocol achieves a reasonable balance between computational and signaling overhead, and it is more suitable for achieving efficient device authentication and privacy protection in virtual power plants.

Iuon‐Chang Lin, Pai-Ching Tseng, Pin-Hsiang Chen, Shean-Juinn Chiou

: In the realm of data management, data preservation stands as a critical undertaking aimed at preserving and upholding the integrity of data. Regardless of whether it concerns personal or enterprise data, the detrimental effects of malicious alterations implemented by attackers cannot be overlooked. Particularly in conventional industrial control environments, the prevalent practice involves the transmission of data from sensors to databases for storage purposes. However, it is essential to recognize that this process exposes the data to various vulnerabilities. Thus, to ensure the long-term security and reliability of the data, it becomes imperative to implement robust data preservation strategies within these industrial control systems. However, the reliance of these databases on physical hard disks introduces inherent vulnerabilities, including the potential for data loss due to disk damage or targeted malicious attacks. Consequently, it becomes imperative to prioritize the implementation of robust data preservation measures. These measures are crucial in mitigating the risk of disruptions and protecting critical data from compromise. By establishing effective data backup systems, employing advanced security protocols, and implementing proactive monitoring mechanisms, organizations can bolster their data preservation capabilities and safeguard against potential threats to data integrity and availability. As a result, many enterprises opt to store their data with third-party providers to ensure data integrity. However, this approach carries inherent risks. If the third-party service experiences an attack or if the data is tampered with, it becomes challenging to verify the integrity of the data. To address these concerns and ensure data preservation within the context of the Internet of Things (IoT), a growing number of individuals are integrating IoT with Distributed Ledger Technology (DLT). By leveraging DLT, the integrity of data can be ensured, reducing reliance on centralized third-party storage and enhancing security in the IoT ecosystem. In this article, IOTA is the DLT, which employs Directed Acyclic Graph (DAG) to store transaction information. Compared to Ethereum or other blockchain technologies, IOTA offers notable advantages in terms of transaction verification speed, making it highly suitable for real-time IoT environments. However, the conventional transmission path from sensors to IOTA nodes entails a complex route, involving multiple hardware devices before reaching the intended destination. This complexity poses challenges in ensuring data integrity during transmission and introduces vulnerabilities such as man-in-the-middle attacks or SQL injection attacks. To address these issues, we propose a method to streamline the transmission path between sensors and IOTA, specifically tailored for industrial fields with numerous IoT devices. Our approach involves preprocessing the data stored on the server using our method before uploading, ensuring data confidentiality, and leveraging IOTA to guarantee data integrity. To achieve the shortest path between IoT and DLT nodes, it becomes necessary to establish IOTA nodes on lower-level devices, such as Raspberry Pi or IoT controllers. By simplifying the transmission path, we can reduce the potential for tampering and enhance overall data security. Implementing our proposed method enables the assurance of data confidentiality and integrity during both transmission and storage on the server, strengthening the trustworthiness of the IoT, and IOTA integration.

Russell Sadeghi, Saeid Sadeghi, Ashkan Memari, Saba Rezaeinejad · 5 authors

No abstract is available for this record.

Peng Zhang, Peilin Wu, Yuhong Liu, Ye Chen · 7 authors

The rapid expansion of transactive energy has transformed traditional electricity consumers into producers, engaging in local energy trading. In the context of distributed energy transactions, blockchain technology has been increasingly applied to facilitate transaction transparency and reliability. However, due to the challenges in collecting accurate energy transmission data from power lines, most existing studies on the blockchain-based transactive energy market are still vulnerable to security attacks, such as malicious users misreporting energy prices, refusing to pay or refusing to transmit energy. Therefore, based on the co-simulation platform PEMT-CoSim and a blockchain, we establish a blockchain-based, reputation-aware secure transactive energy market (STEM) by introducing a reputation scheme to evaluate the trustworthiness of all prosumers and designing reputation-aware, multi-round double auction and energy transmission algorithms to detect and penalize malicious attacks. Furthermore, we run comprehensive experiments for different use cases. The results show that even with malicious participants, the proposed system can guarantee the interests of the honest participants and improve the robustness and effectiveness of the energy market.