Muhammad Faheem, Mahmoud Ahmad AlâKhasawneh, Arfat Ahmad Khan, Syed Hamid Hussain Madni
Blockchain-based reliable, resilient, and secure communication for Distributed Energy Resources (DERs) is essential in Smart Grid (SG). The Solana blockchain, due to its high stability, scalability, and throughput, along with low latency, is envisioned to enhance the reliability, resilience, and security of DERs in SGs. This paper presents big datasets focusing on SQL Injection, Spoofing, and Man-in-the-Middle (MitM) cyberattacks, which have been collected from Solana blockchain-based Industrial Wireless Sensor Networks (IWSNs) for events monitoring and control in DERs. The datasets provided include both raw (unprocessed) and refined (processed) data, which highlight distinct trends in cyberattacks in DERs. These distinctive patterns demonstrate problems like superfluous mass data generation, transmitting invalid packets, sending deceptive data packets, heavily using network bandwidth, rerouting, causing memory overflow, overheads, and creating high latency. These issues result in ineffective real-time events monitoring and control of DERs in SGs. The thorough nature of these datasets is expected to play a crucial role in identifying and mitigating a wide range of cyberattacks across different smart grid applications.
Paraskevas Koukaras, Konstantinos D. Afentoulis, Paschalis A. Gkaidatzis, Aristeidis Mystakidis ¡ 7 authors
This research, conducted throughout the years 2022 and 2023, examines the role of blockchain technology in optimizing Demand Response (DR) within Smart Grids (SGs). It critically assesses a range of blockchain architectures, evaluating their impact on enhancing DRâs efficiency, security, and consumer engagement. Concurrently, it addresses challenges like scalability, interoperability, and regulatory complexities inherent in merging blockchain with existing energy systems. By integrating theoretical and practical viewpoints, it reveals the potential of blockchain technology to revolutionize Demand Response (DR). Findings affirm that integrating blockchain technology into SGs effectively enhances the efficiency and security of DR, and empirical data illustrate substantial improvements in both cases. Furthermore, key challenges include scalability and interoperability, and also identifying opportunities to enhance consumer engagement and foster system transparency in the adoption of blockchain within DR and SGs. Finally, this work emphasizes the necessity for further investigation to address development hurdles and enhance the effectiveness of blockchain technology in sustainable energy management in SGs.
Data-driven modeling using Artificial Intelligence (AI) is envisioned as a key enabling technology for Zero Touch Network (ZTN) management. Specifically, AI has shown huge potential for automating and modeling the threat detection mechanism of complicated wireless systems. The current data-driven AI systems, however, lack transparency and accountability in their decisions, and assuring the reliability and trustworthiness of the data collected from participating entities is an important obstacle to threat detection and decision-making. To this end, we integrate smart contracts with eXplainable AI (XAI) to design a robust cybersecurity framework for ZTN. The proposed framework uses a blockchain and smart contract-enabled access control and authentication mechanism to ensure trust among the participating entities. Additionally, with the collected data, we designed Digital Twins (DTs) for simulating the attack detection operation in the ZTN environment. Specifically, to provide a platform for analysis and the development of an Intrusion Detection System (IDS), the DTs are equipped with a variety of process-aware attack scenarios. A Self Attention-based Long Short Term Memory (SALSTM) network is used to evaluate the attack detection capabilities of the proposed framework. Furthermore, the explainability of the proposed AI-based IDS is achieved using the SHapley Additive exPlanations (SHAP) tool. The experimental results using N-BaIoT and a self-generated DTs dataset confirm the superiority of the proposed framework over some baseline and state-of-the-art techniques. ⢠A new robust cybersecurity framework for ZTN is proposed by integrating smart contracts with eXplainable AI. ⢠To ensure secure communication, a novel blockchain-enabled key establishment and access control mechanism is proposed that authenticates the participating entities in ZTN. The temper-proof property of blockchain ensures high integrity of the data enrichment and builds trust between the participating entities of blockchain network. A smart contract enabled Proof-of-Authority (PoA) consensus mechanism is used to verify and validate the transactions or data. ⢠The authenticated data from smart contract and blockchain-enabled authentication scheme is used to design DT for simulating the attack detection operation in ZTN environment. In particular, the DT is set up with a range of process aware attack scenarios to provide a platform for study and the creation of Intrusion Detection System (IDS). To assess the proposed frameworkâs ability to identify attacks, a Self Attention-based Long Short Term Memory (SALSTM) network is deployed. Additionally, utilizing the SHapley Additive exPlanations (SHAP) tool, the proposed AI-based IDS is made explainable. ⢠Through experiments using an actual DT simulated dataset and state-of-the-art intrusion dataset (N-BaIoT) is used to evaluate the proposed framework. The outcomes are compared with some baseline and state-of-the-art techniques to show the effectiveness of the proposed cybersecurity framework.
Prabhat Kumar, Danish Javeed, Randhir Kumar, A.K.M. Najmul Islam
Summary Artificial Intelligence (AI) based cyber threat detection tools are widely used to process and analyze a large amount of data for improved intrusion detection performance. However, these models are often considered as black box by the cybersecurity experts due to their inability to comprehend or interpret the reasoning behind the decisions. Moreover, AIâbased threat hunting is dataâdriven and is usually modeled using the data provided by multiple cloud vendors. This is another critical challenge, as a malicious cloud can provide false information (i.e., insider attacks) and can degrade the threatâhunting capability. In this paper, we present a blockchainâenabled eXplainable AI (XAI) for enhancing the decisionâmaking capability of cyber threat detection in the context of Smart Healthcare Systems. Specifically, first, we use blockchain to validate and store data between multiple cloud vendors by implementing a Clique ProofâofâAuthority (CâPoA) consensus. Second, a novel deep learningâbased threatâhunting model is built by combining Parallel Stacked Long Short Term Memory (PSLSTM) networks with a multiâhead attention mechanism for improved attack detection. The extensive experiment confirms its potential to be used as an enhanced decision support system by cybersecurity analysts.
Smart grids are getting important in todayâs power management, so with that, smart grid technologies are increasingly important too. There have been a lot of concerns about smart grid technologies being hacked, and as a result, some deep black box adversarial attacks have been conducted and presented. We propose a new experimental methodology for benchmarking smart grid security with black box attacks. Additionally, concerning the type of smart grids, Smart Power Grids, deep black box adversarial attacks which can be crafted using virtually no knowledge about the target due to the inherent complexity of content available in cryptographic libraries like SecLib or Bouncy Castle how it affects security of cyber-physical power systems. We identify potential impacts of deep black box attacks on Smart Power Grids as implemented by the Department of Energy in 1996, we evaluate existing protection methods, and we find out the pitfalls thereof. With the aim of overcoming the aforementioned drawbacks, we initiate a study on deep black box adversarial attacks against Smart Power Grids showing that statistically significant effects against a national Smart Power Grid are achievable with absolute security. We also probe detection of cyber security attacks on Smart Power Grids. We illustrate landscape of smart grids with numerous cyber threats and demonstrate the limitations of traditional security practices. We show the importance of machine learning to detect attacks and the unlikelihood of identification of dependable and efficient detection schemes. We describe quantum voting ensemble models as one of the most powerful techniques in the detection of cyber security attacks. Finally, we propose an experimental setup and evaluation criteria to detect cyber security attacks in smart grids using quantum voting ensemble models. Then, we talk about private data storage in blockchain based smart grid infrastructure. We give an introduction of block chain and its essentiality in smart grids. We discuss privacy issues in block chain based smart grids. We acknowledge the strength of privacy safeguards, but on the same wavelength, we realize their weaknesses. Next, we propose a quantum resistant encryption technique that enhances the privacy of smart grids. We propose quantum voting ensemble models as one of the most promising techniques to address the issue of private data storage in block chains. As a result, we provide a comparison between the proposed models and traditional approaches to privacy protection in smart grids based on an experimental performance review. Then, we propose a unified strategy to improve smart grid cyber security by incorporating deep black box attacks with quantum voting ensemble models. Finally, we disclose several benefits of such integration and perform an experimental evaluation to investigate the effectiveness of the unified approach. The results of our study identify security gaps in smart grids and propose state-of-the-art mechanisms to address them. The challenges of smart grids system require the amalgamation of blockchain, quantum voting ensemble models and deep black box adversarial attacks. We achieve this objective proposing a unified strategy. The results of this study will equally be helpful for future research and smart grid cyber security implementations.
Charithri Yapa, Chamitha de Alwis, Madhusanka Liyanage, Janaka Ekanayake
Blockchain has become the technology enabler in delivering modern Smart Grid 2.0 functionalities. Many services including Peer-to-Peer energy trading, distribution network management, financial settlements, and energy data management are catered through blockchain-enabled platforms. However, areas such as service quality-based pricing strategies, supplyâdemand balancing in distribution system to attain enhanced reliability and consumption-oriented rewarding mechanisms need improving in order to achieve the full benefits of the envisaged grid architecture. In response, this study proposes a novel Blockchain-as-a-Service for Energy Trading (BaaSET) platform, which offers reputation-based services, executed through smart contracts for smart grid applications. Reputation-based grid operations are automatically executed through smart contracts deployed onto a blockchain. The reputation is estimated using power quality and reliability indices, obtained through grid measurements. Further, tests have been conducted to evaluate the associated latency and the implementation cost of the proposed blockchainized service architecture. Test results signify the performance to be comparatively better considering the state-of-the-art. The results further suggest alternatives to improve the scalability of the architecture, to cater the increasing number of stakeholders in the SG 2.0 environment.
The rapid development of physical device-based data collection in emerging technology needs smart, secure, and intelligent transmission. Cyber physical systems compete with the requirement of intelligent transmission of data. In cyber physical systems, security is a very challenging task due to the heterogeneous connections of devices in real time. This paper proposes a novel methodology for cyber-attack finding in cyber physical systems. The proposed system employed a DNN-deep neural network for the categorization of normal and attack data. The employed deep neural network design for 4 hidden layers for the detection of anomalies. For the secured transmission, we employed the blockchain process in Ethereum. The process of Ethereum generates blocks of blockchain with headers and transmits data over the cyberworld to the physical world with the alteration of data. For the authentication of the projected algorithm tested on two real-time datasets, such as NSL-KDD15 and CIDDS_001. The working of proposed algorithm is very promising in compression of existing algorithms of deep learning like RNN-recurrent neural networks, DBN, and DNN.
Godwin C. Okwuibe, Thomas Brenner, Muhammad Yahya, Peter Tzscheutschler ¡ 5 authors
Abstract Blockchainâbased local energy markets have been proposed in recent years to provide a market platform for local prosumers and consumers to exchange their energy in a secured, transparent and tamperâproof manner. However, there are still some challenges regarding the scalability of blockchain to handle high computational models/algorithms/contracts as this may result in the extension of the block size of the blockchain network and very high gas costs. Also, there is still the problem of transparency as regards General Data Protection Regulation because the full visibility of data in the blockchain may collide with privacy in some settings. A framework is presented that combines the onâchain features of blockchain with trusted execution environments to develop a transparent, tamperâresistant, low operation cost, scalable and resilient hybrid model architecture for local electricity trading. The model architecture was simulated in German community case scenarios for a varying number of prosumers and consumers to show its applicability. The simulation results show that the model was able to solve the scalability problem of blockchain for the local energy market application as the market model is run in a trusted environment where the integrity of the model can be verified by the participants.
Muhammad Faheem, Heidi Kuusniemi, Bahaa Eltahawy, Muhammad Shoaib Bhutta ¡ 5 authors
Abstract Energy is a crucial need in today's world for powering homes, businesses, transportation, and industrial processes. Fossil fuels, such as oil, coal, and natural gas, have been the primary sources of energy for decades. However, there is growing recognition of the negative environmental impact of fossil fuels and the need to transition to cleaner and more sustainable sources of energy. Distributed Energy Resources , such as wind and solar offer several benefits including, reducing energy costs, increasing resiliency, and decreasing carbon emissions. However, the integration of () into the grid requires advanced communication and secure control strategies to ensure a stable and reliable grid operations. In this regard, a blockchainâbased industrial wireless sensor network can provide secure and resilience data transmission to facilitate intelligent integration, monitoring, and control of in the smart grid. In this research, a smart contracts framework in Solana called Advanced Solana Blockchain () is proposed for in the smart grid. The proposed scheme enables resilient and secure realâtime control and monitoring of in smart grids. The performance evaluations and security analysis illustrated that this scheme is secure, reliable, and suitable in terms of lightweight data sharing between in smart grids.
The Internet of Things (IoT) has advanced smart grid (SG) infrastructure by providing smart meters (SMs) with enhanced capabilities such as the ability to leverage the Internet platform for bidirectional information exchange. Cryptographic keys are necessary for securely exchanging sensitive information between SMs and energy providers. To manage these keys, a secure key management protocol (KMP) with little overhead and influence on the SGâs overall performance is necessary. Although various KMPs are available for IoT-enabled SG environments, exiting solutions have several flaws in terms of certificate revocation, security requirements, and overall SG performance. To address these challenges, this paper proposes a blockchain-based computationally-efficient and highly-secure KMP for IoT-enabled SG environments. We show that, compared to existing solutions, the proposed KMP has better SM side efficiency with improved security and more properties such as perfect forward secrecy, conditional anonymity, and simple SM revocation.
This proceeding volume has been retracted from the publication because we found some solid reasons to believe that it has infringed our integrity criteria and now presents a risk for our journal and scholarly science in general. Different types of malpractice are involved, in particular citation manipulation and inappropriate references. We are extremely concerned by such malpractice which considerably impacts the image of our title and our Publisherâs reputation. For further details, please refer to our publishing ethics policies . If you have any questions, please contact us at contact@webofconferences.org See the retraction notice E3S Web of Conferences 505, 00001 (2024), https://doi.org/10.1051/e3sconf/202450500001
Nabil Tazi Chibi, Omar Ait Oualhaj, Wassim Fassi Fihri, Hassan El Ghazi
Smart Grids (SGs) rely on advanced technologies, generating significant data traffic across the network, which plays a crucial role in various tasks such as electricity consumption billing, actuator activation, resource optimization, and network monitoring. This paper presents a new approach that integrates Machine Learning (ML), Blockchain Technology (BT), and Markov Decision Process (MDP) to improve the security of SG networks while ensuring accurate storage of events reported by various network devices through BT. The enhanced version of the Proof of Work (PoW) consensus mechanism ensures data integrity by preventing tampering and establishing the reliability of known and unknown attack detection. The proposed versions of PoW, namely GPoW 1.0 and GPoW 2.0, aim to make the consensus process more environmentally friendly.
M. Anwar, Noshina Tariq, Muhammad Ashraf, Syed Atif Moqurrab ¡ 7 authors
Cybersecurity challenges pose a significant threat to Healthcare Cyber Physical Systems (CPS) because they heavily rely on wireless communication. Particularly, jamming attacks can severely disrupt the integrity of these CPS networks. This research introduces a decentralized system to address this issue. Therefore, this paper suggested a system that leverages trust and blockchain technology to detect jamming attacks in healthcare CPS effectively. It proposes a layered model to improve CPS networksâ lifetime and performance. In smart healthcare environments, it ensures secure and reliable communication between sensor nodes, wearable sensors, medical devices, and monitoring systems. Results show that the suggested approach outperforms the baseline model in identifying and minimizing jamming assaults, with an average percentage difference of 15.71% more detection rate, 20.21% less packet loss rates, 16.65% less node-level energy consumption, reduced network latency of 8.29%, and 9.63% more network throughput.
Yan Li, Yan Li, Yuying Gong, Mingbo Wu ¡ 7 authors
Abstract Blockchain technology is demonstrating vast potential in the realm of distributed energy transactions. This article employs the Ethereum platform to architect an energy trading settlement framework. By incorporating whitelists, authorization mechanisms, and dedicated energy tokens, transactions are rendered more secure and reliable, ensuring the openness, transparency, traceability, and immutability of transaction information. The proposed transaction settlement mechanism automatically updates users' energy imbalances and levies corresponding charges, thereby ensuring strict adherence to transaction contracts. Furthermore, during the settlement phase, smart contracts are employed to autonomously evaluate the creditworthiness of transaction parties, with results recorded on the blockchain. As the credit levels of the entities across the entire network increase, the block generation time for distributed energy trading blockchains becomes shorter, with the block generation time predominantly influenced by the credit rating of the highest-rated entity in the network. Hence, through smart contract design based on energy trading, blockchain technology is utilized to enhance the transparency and security of transaction settlement and clearing. Future research could focus on improving blockchain scalability, integrating emerging technologies, to advance the effectiveness and adoption of blockchain-based energy trading systems.
Akshay Chaudhary, Prateek Negi, Amit Dimari, Rohan Rohan
Energy suppliers, entrepreneurs, technological developers, financial organizations, national governments, and academics are interested in blockchains or distributed ledgers. Many of these sources believe blockchains may provide considerable advantages and innovation. Smart contracts and blockchains offer transparent, tamper-proof, and secure platforms that may allow new business solutions. This article covers core blockchain topics including system designs and distributed consensus techniques. From peer-to-peer (P2P) energy trading and Internet of Things (IoT) applications to decentralized markets, electric car charging, and e-mobility, opportunities, problems, and constraints are examined.
As the world increasingly adopts renewable energy, the importance of smart grids grows. Integrating renewable sources into the current grid presents technical and economic challenges. Blockchain technology offers a promising solution by enabling decentralized energy trading, allowing efficient exchanges between producers and consumers. This paper proposes a detailed framework for integrating blockchain into the smart grid. The framework includes developing a blockchain-based platform for energy trading, creating smart contracts to manage and automate transactions, and incorporating IoT devices for energy data collection and sharing. By leveraging blockchain's transparency, security, and decentralization, our proposed system aims to address inefficiencies and foster a more resilient energy infrastructure. This approach could enhance the sustainability and reliability of energy distribution, supporting the transition to renewable sources. We believe our framework has the potential to significantly improve the efficiency and sustainability of the smart grid, paving the way for a more robust and adaptive energy system capable of meeting future demands.
In response to global efforts to deal with climate change, various renewable energy policies are being implemented. Among these, renewable portfolio standards (RPS) and renewable energy 100 (RE100) regulate the obligated supply of renewable energy to ensure compliance with set quotas by nations and institutions. In this context, the renewable energy certificate (REC) system is employed to assist obligated entities in meeting their renewable energy generation quotas. Obligated entities can purchase REC from renewable energy generators to obtain recognition for their renewable energy allocation. However, the current REC system is insufficient in addressing issues related to procedural complexity and cyber security. This study aims to overcome its limitations by applying the blockchain technology. Blockchain, a distributed financial network, serves as a digital ledger, enabling peer-to-peer transactions in a simple, transparent, and secure way. The proposed new REC system based on blockchain moves away from the complex structure of the traditional REC system, simplifying the system into four processes: participation, issuance, transaction, and authentication. Moreover, by applying blockchain algorithms, it addresses the cyber security issues of the traditional system. The case study using Hyperledger Besu, or one of blockchain platforms, demonstrates that the aspects of procedural complexity and cyber security are improved in the proposed system.
Mateo D. Roig Greidanus, GabâSu Seo, Sudip K. Mazumder
This paper presents a unified multi-timescale control approach for a power system with distributed energy resources to achieve cyber-resilient operation. The proposed concept combines two cyber-resilient control methods: proactive and reactive methods. The proactive method uses a blockchain that ensures measurement and control data can be securely exchanged among grid assets and also derives control set points as a load-sharing supervisory control, with an embedded logic called chaincode. The proactive method ensures data integrity, but it inherits stochastic latency with significant standard deviation due to the nature of the distributed ledgers and security measures, leading to challenges in control. To overcome this trade-off, the reactive approach uses event-driven communication. For this approach, rather than communicating the complete data, a lightweight data packet is communicated in a peer-to-peer fashion. Therefore, it guarantees driving the system into a stable operation in case the proactive operation degrades with insufficient latency. To validate the concept, Hyperleger Fabric blockchain 2.2 is used to characterize the latency and is customized for an inverter control system in this study. Based on the use case, a stability analysis is presented to evaluate the impact of the variable delay and to identify the need for a reactive approach to mitigate the effects of a prolonged delay in the proactive method. A test bed with two hardware inverter prototypes and a custom blockchain programmed with the unified method is developed for validation. A set of hardware experimental results validates the methodology and demonstrates the inverter system operations achieving frequency recovery and load-sharing restoration based on the unified control method.
As the deployment of IPv6 networks continues to expand, managing security threats becomes increasingly intricate due to the protocolâs extensive address space and dynamic traffic patterns. This paper presents a novel blockchain-driven decentralized anomaly detection algorithm designed explicitly for IPv6 networks. By leveraging the inherent properties of blockchainâimmutability, transparency, and decentralizationâour approach enhances security monitoring capabilities. Integrating traffic analysis with a distributed ledger facilitates improved accuracy in anomaly detection and robust resilience against distributed denial-of-service (DDoS) attacks and other threats. Experimental evaluations conducted in a simulated IPv6 environment demonstrate that the proposed methodology outperforms traditional centralized detection systems, significantly improving detection accuracy, attack mitigation, and data integrity.
Haotian Deng, Tao Liu, Xiaochen Ma, Weijie Wang ¡ 7 authors
The space-air-ground integrated networks (SAGINs) are pivotal for modern communication and surveillance, with a growing number of connected devices. The proliferation of IoT devices within these networks introduces new risks due to potential erroneous synergistic interactions that could compromise system integrity and security. This paper addresses the challenges in coordination, synchronization, and security within SAGINs by introducing a novel static program analysis (SPA) technique using zero-knowledge (ZK) proofs. This approach ensures the detection of risky interactions without compromising sensitive source code, thus safeguarding intellectual property and privacy. The proposed method overcomes the incompatibility between SPA and ZK systems by developing an imperative programming language for SAGINs and a specialized abstract domain for interaction threats. The system translates network control algorithms into arithmetic circuits suitable for ZK analysis, maintaining high accuracy in detecting risks. Evaluations of real-world scenarios demonstrate the systemâs efficacy in identifying risky interactions with minimal computational overhead. This research presents the first ZK-based SPA scheme for SAGINs, enhancing security and confidentiality in network analysis while adhering to privacy regulations.
Aakanksha Bedi, J. Ramprabhakar, R. S. Anand, U Kumaran ¡ 6 authors
Smart grids (SGs) are technology-powered electricity networks that support bidirectional power and data flows. This allows real-time monitoring of demand and enhances the gridâs capability to dynamically adjust the generation and reduce the gap between supply and demand. However, implementing a smart grid in the power network comes with its own set of security challenges, such as cyber-security, distrust in participants, and lack of customer engagement due to various cyber-attacks. Such cyber-attacks will create distrust among consumers/prosumers to adopt the smart grid and distributed energy resources (DER) framework. To circumvent this, a blockchain-supported hybrid authentication and handshake algorithm (BSHAHA) for smart grids is proposed in this work, which authenticates data communication between peer-to-peer, aggregators, virtual power plants, and the grid. The algorithm was developed incorporating elliptic-curve cryptography (ECC) and advanced encryption standards (AES) to enhance privacy and session security. The proposed algorithms are verified and tested using formal cyber-security tools, such as the Random oracle model and AVISPA as well as by informal security analysis. Furthermore, to simulate a real-time test environment, this paper utilized ns-3 network simulator to simulate different smart meter scenarios, and the proposed algorithms are tested for power consumption and scalability, and results are presented. Moreover, blockchain simulation was first done in the local blockchain using Ganache and Truffle IDE and later using the Holesky Ethereum test network and remix-IDE. Lastly, this paper presented a comparison analysis of power consumption for different consensus mechanisms.
In the field of Artificial Intelligence (AI), there is an increasing focus on enhancing trustworthiness especially in critical sectors such as in the management of civil infrastructure. This paper proposes the adoption of a framework based on Hybrid Distributed Ledger Technology (Hybrid-DLT) as a technological solution for improving trustworthiness. We detail three specific applications in the sector of critical infrastructure maintenance: Explainable AI (XAI) for risk classification, structural defects recognition, and real-time monitoring through IoT. The proposed approach employs tamper-resistant ledgers for tracking key processes such as dataset collection, model training, and inference generation, thereby ensuring non-repudiability for recorded actions and enabling auditability. We demonstrate how this strengthens the explainability mechanisms of AI models and enables the production of verifiable data lineage and certified inferences. Our framework can be applied to existing AI solutions, enhancing their trustworthiness.
The rapid advancement of grid modernization and the proliferation of smart grids have engendered a critical need for cyber-physical security. Recent cyber-attacks targeting grid infrastructure, notably leading to substantial blackouts in Ukraine, underscore the vulnerabilities and potentially catastrophic consequences of such incursions. These attacks, whether stemming from cyber threats such as Denial of Service (DOS), False Data Injection Attacks (FDIA), or complex cyber-physical manipulations, emphasize the imperative of robust cybersecurity protocols in smart grid operations. This research investigates a pivotal approach to fortify and safeguard smart grid systems by integrating blockchain technology with wireless sensor nodes. By leveraging a Proof of Authority (PoA) Ethereum Blockchain framework, the study delves into the transformative capabilities of Blockchain within Supervisory Control and Data Acquisition (SCADA) networks. Specifically, it examines configurations across IEEE 14-bus, 30-bus, and 118-bus topologies. In addition to elucidating the inherent vulnerabilities in traditional SCADA systems, this study meticulously evaluates an array of performance matrices. Statistical analyses encompassing mean, standard deviation, skewness, kurtosis, and confidence levels provide nuanced insights into the efficacy of blockchain mechanisms in enhancing SCADA resilience against contemporary cyber threats. This research endeavors to bridge the gap in modern cybersecurity paradigms by fusing blockchain technology with wireless sensor nodes. By fortifying data integrity, elevating the reliability of data transmission, and augmenting trustworthiness within SCADA infrastructures, this study aims to present robust solutions to the escalating cybersecurity challenges faced by smart grid systems.
DucâMinh Ngo, Dominic Lightbody, Andriy Temko, Colin C. Murphy ¡ 5 authors
With the widespread integration of new technologies, IoT devices are becoming increasingly diverse and capable of handling highly complex tasks, compared to previous generations. This evolution has led to demands for a comprehensive security approach across multiple layers of an IoT architecture. This work proposes a scalable security solution from the edge to the cloud, combining Blockchain technology and anomaly-based Intrusion Detection Systems (IDSs). Smart contracts provide a transparent environment for registering and managing IoT devices on the cloud. Specifically, the smart contract includes two authorization levels for managing administrators and IoT devices. Besides, anomaly-based IDSs are deployed at Gateways to detect network attacks. We propose using lightweight machine learning models on FPGA hardware acceleration for Gateways. We have simulated the Blockchain network on the Ganache software, demonstrating that the smart contract effectively manages administrators and devices such that only authorized entities can access the system. The FPGA-based Gateway, which contains pre-trained Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) detection models from the IoT-23 dataset, has been deployed on the Alveo U280 card. The ANN model has achieved the highest processing speed at 20Gbps. The results indicate that integrating Blockchain and anomaly-based IDS significantly enhances scalable security in IoT networks.