Smart grid technology is an amicable improvement of the conventional power grid characterized by improved communication, control, and computing technologies that enhance improved energy distribution. A smart grid is an improvement on the existing electric grid system that allow for more intelligent controlling of electricity from the generation point right down to the consumer. Consequently, the ICS (Industrial Control Systems) of smart grids have become more exposed to cyber risks resulting from enhanced network integration and digitalization. The complexity of smart grids with many DERs (Distributed Energy Resources), sensors, and systems make the security problem challenging. Reasons why decentralised smart grids have to be secure and more resilient mean that new approaches that can enable detection, prevention, and mitigations of cyber-attacks are desirable. Deep learning and smart grid cybersecurity based on decentralization has a bright outlook as it enables improving the detection of anomaly cases and potential threats and increasing the general level of resilience of the grid.Smart grids are a major evolution of conventional power grids that employ ICT (Information and Communication Technology) to optimize the delivery of electrical energy. Elements of smart grid include smart meters for consumers, automated distribution network, and communication network. Smart grids can be decentralised as it includes multiple DERs like solar power, wind mills or energy storage systems, that are usually integrated at the outskirts of the smart grid. Such decentralization adds more challenges to the grid's physical structure and also adds more vectors by which a cyber-threat can penetrate the network [1]. As such, cybersecurity emerged as a focal topic to protect the safe and reliable functioning of smart grids. It has also shown a commanding success in several contexts, which is due to the deep learning's inclusion capabilities of key features from accesses data [2]. A major advantage of deep learning is that models are able to detect the abnormal flow of traffic since they hold knowledge of normal traffic flow patterns [3]. Pattern recognition is another important factor; deep learning networks can recognize even complex pattern in a given data. However, deep learning models include scalability hence making them capable of analyzing a large quantity of data produced by, for instance, smart grids [4].The decentralized smart grids are the most vulnerable because of the localized architecture and large connections with IoT gadgets. The different systems that are used in smart girds are not homogenous and have different protocols and hence the weakness are provided [5]. Lack of computational capacity of many IoT devices due to resource constraints precludes such approaches and traditional security techniques cannot be implemented [6].Despite the potential of deep learning in smart grid cybersecurity, there are a number of issues before it. Concerning a few key points, it is important to mention data confidentiality as the training data can be considered sensitive. Another, there is an interpretability problem since, unlike traditional machine learning techniques, deep learning's models are considered 'black box' [7]. Another limitation of deep learning is that it demands massive computation, which may well not be readily feasible in low-power devices of smart grid [8]. There is also a big issue related to integration with legacy systems because such systems might be incompatible with deep learning solutions [9].Future research directions encompass the development of Smart grids which are decentralized and also experience a higher level of risk with relation to cybersecurity because of the deployment of several IoT devices. Table 1 summarizes robust deep learning approaches for smart grid cybersecurity, highlighting their advantages, challenges, and future directions. While these methods show high accuracy in detecting cyber threats (ranging from 92% to 99.5%), they face issues like high computational demands, vulnerability to adversarial attacks, and scalability concerns. Future research focuses on improving real-time integration, enhancing model interpretability, and developing more robust AI-driven cybersecurity frameworks.The most suitable solution for smart grid cybersecurity protection combines Federated Learning with Blockchain and Adversarial Deep Learning. With FL the grid nodes can participate in decentralized training processes without exchanging actual data which protects their information security and privacy. The combination of Blockchain technology and adversarial training creates an unalterable security framework which secures communication while building resistance against complex cyber threats. The implementation of this combined method becomes necessary because smart grids function through decentralized systems that connect many vulnerable IoT-enabled energy production networks to cyber security threats. Maximum security models become ineffective because they suffer from dimensional problems alongside privacy weaknesses and developing electronic strike threats. Through an integration of FL and Blockchain technology organizations achieve real-time threat detection with adaptive capabilities and lower IT overhead costs. Industrial security in the energy sector needs sophisticated AI-enabled solutions which must scale effectively to defend against infrastructure attacks and disruption of power supply. By utilizing this model organizations maintain autonomous cybersecurity operations which produce efficient proactive threat protection suitable for advanced smart grid systems against developing cyber attacks. Deep learning in the decentralised smart grid cybersecurity is a revolutionary way of handling the huge and dynamic risks. Based on the real-time data processing characteristic and the ability to recognize patterns of deep learning models, it is possible to improve the density of anomaly detection, threats' prediction, and systems' robustness. However, there are challenges that the use of deep learning in this area holds among them the fact that it calls for usage of a lot of computational power, data security issues, and the issues related with initiation and incorporation of integration of such complex technologies in the existing systems. To overcome these challenges new approaches, need to be created more efficiently, focus on to build effective privacy preservations, and integrate with other existing systems. For future work, the focus should be made on introducing new sophisticated and flexible frameworks of deep learning for the smart grid that will function in the given distributed environment. In this way, the industry can progress and advance towards building a smarter grid, that can address novel cyber threats and protect and enhance the reliability of the energy distribution systems.
Minlibe LAMBONI, Eyouléki Tcheyi Gnadi Palanga, Kossi TEPE
The management of smart grids requires enhanced transparency and efficient optimization of energy transactions. While blockchain technology is widely used to ensure traceability and decentralization, existing solutions primarily focus on commercial aspects, often overlooking detailed monitoring of energy flows. This study proposes a hybrid blockchain model combining Ethereum and Hyperledger Fabric to integrate secure transaction execution with real-time energy flow tracking. The adopted approach involves identifying key components of decentralized energy management, conducting a comparative analysis of blockchain architectures, and performing experimental simulations to evaluate their performance in terms of latency, security, and scalability. Specific metrics, such as transaction throughput, block validation time, and energy data granularity, were utilized to assess the efficiency of the proposed model. The results demonstrate that Hyperledger Fabric excels in energy flow monitoring and auditability, whereas Ethereum optimizes transaction execution through its consensus mechanism and broad adoption. The integration of both technologies enables optimal complementarity, ensuring effective interoperability and significantly improving overall system transparency and efficiency. The proposed hybrid model establishes a scalable and resilient architecture that enhances coordination among network participants and optimizes energy governance. It fosters trust among stakeholders by ensuring the integrity and immutability of exchanges while enhancing the management of distributed energy resources. By integrating Ethereum and Hyperledger Fabric, this solution provides an innovative and applicable framework for decentralized energy infrastructures, optimizing transaction management, improving energy flow traceability, and reinforcing the resilience of smart grids against increasing demands for flexibility and sustainability.
The integration of blockchain , Internet of Things devices, and distributed energy resources is revolutionizing peer-to-peer energy trading by enabling decentralized, efficient, and transparent transactions. However, existing solutions face challenges related to privacy, interoperability, and scalability. This paper presents PrGChain, a privacy-preserving blockchain-enabled energy trading framework within the smart grid, incorporating a decentralized ZKOracle to securely connect blockchain networks with off-chain energy data sources . The proposed ZKOracle employs zero-knowledge proofs to verify energy data without exposing sensitive information , ensuring compliance with privacy regulations, while leveraging a distributed network of oracle nodes for enhanced reliability and interoperability. To improve security and efficiency, PrGChain utilizes smart contracts , decentralized applications (dApps), and leverages stablecoins to mitigate cryptocurrency volatility. Performance evaluations demonstrate that our system achieves improved decentralization and privacy without sacrificing efficiency. This is particularly true when deployed on Layer 2 blockchain networks like Polygon, where transaction latency and costs are significantly reduced.
Aakanksha Bedi, J. Ramprabhakar, R. Anand, Veerpratap Meena · 5 authors
Energy is the basic prerequisite of any industry, but global warming, climate change, and pollution are increasing due to digitization and industrialization. Rising electricity demand, combined with the integration of electric vehicles, has made it increasingly challenging to rely solely on conventional centralized power systems. To meet this current changing energy scenario, it becomes essential to introduce green energy into conventional power systems and allow bi-directional energy flow. Microgrids, smart grids, and virtual power plants will play an important role in making this massive shift from a centralized system to a decentralized power system. A virtual power plant is a cloud-based energy system incorporating various microgrids, energy storage, distributed energy resources, and weather forecasting. Since this system is virtual, it could lead to cyber threats. To the best of the authors’ knowledge, this review article complies with recent data from ten major research libraries, offering consolidated insights into the virtual power plant (VPP) framework that will enhance customer participation and encourage them to become prosumers. Additionally, a blockchain-based VPP framework is presented along with two very prominent scenarios of blockchain-based distributed VPP involving P2P transactions and NW trading discussed for building futuristic NZEGs aimed at reducing carbon footprints and providing a foundation for future net-zero energy grids (NZEZs).
Aditya R. Malhotra1, Kavya S. Ahuja2, Vihaan P. Bansal3, Tanvi R. Kapoor4
The integration of blockchain technology, particularly Bitcoin-inspired decentralized protocols, into smart grid systems offers innovative solutions for energy management, security, and peer-to-peer (P2P) energy trading. Traditional centralized grids face challenges including inefficiencies, security vulnerabilities, and lack of real-time transactional transparency. By leveraging Bitcoin-like blockchain mechanisms, smart grids can facilitate secure, automated, and auditable energy transactions between distributed producers and consumers. This paper explores the design principles of Bitcoin-enabled smart grids, including consensus protocols, cryptographic transaction verification, and integration with IoT-based energy meters. It also examines case studies and simulation models demonstrating the potential for reduced energy losses, enhanced security, and decentralized grid optimization. Finally, challenges related to scalability, transaction speed, and energy consumption of blockchain networks are discussed, highlighting directions for future research in sustainable and efficient decentralized energy systems.
Federated cyber–physical systems (CPSs) present unique security challenges due to their distributed nature and the need for secure communication between components from different administrative domains. Distributed ledger technology (DLT) offers a promising approach to implementing a resilient authentication and authorization mechanism and an immutable record of CPS identities and transactions in federated environments. However, using Distributed Ledger (DL) within a CPS raises some important questions regarding scalability, throughput, latency, and potential bottlenecks, which require effective modeling of DL performance. This paper proposes a novel approach to modeling distributed ledgers using Colored Timed Petri Nets (CPNs). We focus on the performance modeling of Hyperledger Fabric (HLF), a permissioned distributed ledger technology which provides a backbone for a Lightweight Authentication and Authorization Framework for Federated IoT (LAAFFI), a novel framework for secure communication between CPS devices. We implement our model using CPN Tools, a widely adopted CPN modeling software that provides advanced simulation, analysis, and performance monitoring features. Our model offers a robust framework for studying distributed ledger systems’ synchronization, throughput, and response time. It supports flexibility in modeling transaction validation and consensus algorithms, which provides an opportunity for adapting the model to future changes in HLF and modeling other DLs. We successfully validate our CPN model by comparing simulation results with experimental measurements obtained from a LAAFFI prototype.
The integration of Zero-Trust Architecture (ZTA) and Blockchain-based Security Models in IoT-driven industrial power electronics systems has emerged as a transformative approach to mitigating cyber threats and ensuring robust access control. Traditional security mechanisms, which rely on perimeter-based defenses, are increasingly ineffective against advanced persistent threats (APTs), insider attacks, and lateral movement techniques within industrial IoT (IIoT) environments. Zero-Trust security enforces continuous verification, least-privilege access, and micro-segmentation, ensuring that no device or user was inherently trusted. Implementing ZTA in resource-constrained IoT ecosystems presents significant challenges related to computational overhead, authentication latency, and secure data transmission. To address these limitations, blockchain technology enhances decentralized identity management, immutable access logs, and tamper-resistant security frameworks, fortifying Zero-Trust-based access control. Privacy-preserving cryptographic techniques, including zero-knowledge proofs (ZKPs) and homomorphic encryption, safeguard sensitive industrial data while maintaining compliance with evolving regulatory frameworks. AI-driven anomaly detection models reinforce continuous authentication and behavior-based threat monitoring, enabling proactive defense mechanisms against zero-day exploits and sophisticated cyber intrusions. This chapter presents a comprehensive analysis of Zero-Trust implementation models for IIoT systems, highlighting the role of secure communication protocols, distributed ledger-based identity verification, and adaptive security automation. The integration of blockchain-enabled access control and AI-powered real-time security analytics ensures a resilient security posture for industrial power electronics networks, mitigating risks associated with unauthorized access, data breaches, and operational disruptions. The proposed framework enhances scalability, privacy, and computational efficiency, paving the way for next-generation cybersecure industrial ecosystems.
Traditional finance and crypto aren't just different systems-they're different paradigms speaking different languages. Traditional banks measure processes in days, compliance in paperwork, and access in restrictions. Cryptocurrency platforms promise instant transactions but often at the cost of compliance frameworks that traditional institutions require. These systemic limitations in both traditional and cryptocurrency systems highlight the critical need for a unified approach. Using STORE's decentralized cloud computing protocol as an example, this paper emphasizes a different future as it demonstrates the feasibility and impact of automated compliance through the transformative CLEAR framework. Our dual-database architecture achieves authenticated regulatory compliance at global scale, with our implementation achieving 55.6 seconds (verified) for complete end-to-end transactions, with KYC verification (18.84s), document processing (14.24s), and payment settlement (6.55s). This transforms what traditional finance considers a weeks-long journey into a seconds-long verification, without compromising the compliance standards that make global finance possible. It's not just faster-it's fundamentally re-imagined.
This comprehensive article explores the transformative impact of blockchain technology on energy trading risk management systems. The article examines how blockchain addresses critical challenges in data security, transparency, and regulatory compliance within the energy sector. Through detailed analysis of distributed ledger infrastructure, smart contract integration, and cryptographic security measures, the research demonstrates significant improvements in operational efficiency, transaction processing, and risk mitigation. The investigation encompasses automated compliance frameworks, data privacy mechanisms, and scalability solutions, highlighting how blockchain technology enhances market participation while reducing operational costs. The article also evaluates emerging technologies and industry standards, providing insights into future developments that will shape secure and efficient energy trading operations.
Dong Liu, Juan S. Giraldo, Peter Pálenský, Pedro P. Vergara
Model-free power flow calculation, driven by the rise of smart meter (SM) data and the lack of network topology, often relies on artificial intelligence neural networks (ANNs). However, training ANNs require vast amounts of SM data, posing privacy risks for households in distribution networks. To ensure customers' privacy during the SM data gathering and online sharing, we introduce a privacy preserving PF calculation framework, composed of two local strategies: a local randomisation strategy (LRS) and a local zero-knowledge proof (ZKP)-based data collection strategy. First, the LRS is used to achieve irreversible transformation and robust privacy protection for active and reactive power data, thereby ensuring that personal data remains confidential. Subsequently, the ZKP-based data collecting strategy is adopted to securely gather the training dataset for the ANN, enabling SMs to interact with the distribution system operator without revealing the actual voltage magnitude. Moreover, to mitigate the accuracy loss induced by the seasonal variations in load profiles, an incremental learning strategy is incorporated into the online application. The results across three datasets with varying measurement errors demonstrate that the proposed framework efficiently collects one month of SM data within one hour. Furthermore, it robustly maintains mean errors of 0.005 p.u. and 0.014 p.u. under multiple measurement errors and seasonal variations in load profiles, respectively.
Paula Heess, Stefanie Holly, Marc-Fabian Körner, Astrid Nieße · 9 authors
Abstract The need to harness the flexibility of small-scale assets for system stabilization, including redispatch, is growing rapidly with the increasing prevalence of distributed generation, such as photovoltaic systems and heavy loads, in particular heat pumps and electric vehicles. Integrating these resources into the redispatch process presents special requirements: On the one hand, building trust with the owners of such assets requires privacy and a reasonable degree of autonomy and engagement. On the other hand, besides the system’s scalability and robustness, the verifiability and traceability of provided data are essential for grid operators who depend on the reliable provision of redispatch services. To date, research and practice have encountered significant challenges in defining a system that enables the inclusion of decentralized flexibilities while satisfying necessary requirements. To that end, we present a novel conceptual system design that addresses these challenges by combining a multi-agent system (MAS) approach with verifiable information flows through digital self-sovereign identities (SSIs) and Zero-Knowledge-Proofs (ZKPs). Single agents, as edge devices, operate locally and autonomously, respecting customer preferences, while MAS provide the ability to design robust, reliable, and scalable systems. SSI enables agents to manage their data autonomously, while ZKPs are used to protect users’ privacy through selective data disclosure which allows the verification of the correctness of information without disclosing the underlying data. To validate the feasibility of this design, a case study is included to demonstrate the functionality of key sub-processes, such as baseline optimization, aggregation, and disaggregation, in a realistic scenario. This case study, supported by a prototype implementation, provides initial evidence of the concept’s soundness and lays the groundwork for future evaluation through extensive simulations and field testing. Together, the technologies included in the conceptual system design balance full transparency for grid operators with autonomy and data economy for asset owners.
As IoT continues to expand, the security of connected devices remains a critical concern, particularly in the face of DDoS attacks. This study introduces a novel approach that leverages blockchain technology through smart contracts integrated with an advanced attack detection mechanism. Central to this approach is the Enhanced Residual Gated Recurrent Unit (ERGRU) architecture, designed to effectively identify and mitigate DDoS attacks within IoT networks. The Adaptive Coati Optimization Algorithm (ACOA) was used to adjust the hyperparameters of the ERGRU model, such as the learning rate and the number of GRU neurons, to further improve detection accuracy. In addition, the proposed framework uses a one-way compression function to generate secure hashes for input data, utilizing the Merkle-Damgård cryptography technique to ensure data integrity and confidentiality. The proposed solution was tested through a rigorous process using a DDoS dataset. Performance was assessed by focusing on metrics such as processing time, data integrity rate, and confidentially rate. The results demonstrate the effectiveness of the proposed smart contract-based framework in providing a durable and efficient protection mechanism against DDoS attacks in IoT environments.
Decentralized physical infrastructure networks (DePINs) are an emerging vertical within "Web3" replacing the traditional method that physical infrastructures are constructed. Yet, the boundaries between DePIN and traditional method of building crowd-sourced infrastructures such as citizen science initiatives or other Web3 verticals are not always so clear cut. In this work, we systematically analyze the differences between DePIN and other Web2 and Web3 verticals. For this, the study proposes a novel decision tree for classifying systems as DePIN. This tree is informed by prior studies and differentiates DePIN from related concepts using criteria such as the presence of a three-sided market, token-based incentives for supply, and the requirement for physical asset placement in those systems. The paper demonstrates the application of the decision tree to various blockchain systems, including Helium and Bitcoin, showcasing its practical utility in differentiating DePIN systems. This research offers significant contributions towards establishing a more objective and systematic approach to identifying and categorizing DePIN systems. It lays the groundwork for creating a comprehensive and unbiased database of DePIN systems, which will inform future research and development within this emerging sector.
In the realm of decentralized applications, smart contracts play a pivotal role in managing an extensive array of digital assets within blockchain networks. Ensuring the security of these digital assets hinges upon the adept detection of vulnerabilities present within smart contracts. Extensive research efforts have scrutinized and elucidated numerous smart contract vulnerabilities. However, certain vulnerabilities, including signature malleability, hash collision, and inconsequential code segments, remain relatively unexplored and devoid of dedicated detection tools. In response to this research gap, this paper addresses these three previously understudied vulnerabilities. We contribute to the field by creating a labeled dataset comprising vulnerable smart contracts. This dataset serves as a valuable resource for further scientific inquiries, enabling the testing and validation of various detection frameworks. Additionally, we present SmartSentry a static vulnerability detection framework capable of identifying these vulnerabilities. Using both dataflow and control flow analysis, our framework exhibits exceptional performance, successfully identifying labeled vulnerabilities and real-world vulnerabilities within production smart contracts with speed and efficiency. These efforts collectively enhance our understanding of smart contract vulnerabilities and contribute to the broader advancement of blockchain security.
Yunusa Ishaq, Ajuru Success Prince, Gbah Gonto Jean Claude, Togola Molobaly Di Bebe
Abstract: The transition to smart grids has revolutionized energy distribution, enabling more efficient and flexible power management through advanced communication and control systems. However, this interconnected structure makes smart grids vulnerable to cyberattacks, such as False Data Injection Attacks (FDIA), Distributed Denial of Service (DDoS) attacks, and data manipulation. These threats undermine the stability and reliability of the grid, and existing centralized security frameworks are often ill-equipped to address them due to their susceptibility to single points of failure and limited scalability. To overcome these challenges, this paper introduces a decentralized security framework that combines blockchain technology with machine learning (ML). The framework leverages blockchain to provide a transparent, immutable, and decentralized ledger, employing consensus mechanisms like Practical Byzantine Fault Tolerance (PBFT) or Proof of Authority (PoA) to ensure secure data validation. Alongside this, ML models, including Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN), are used to detect anomalies in time-series data, such as FDIA, with high precision. Smart contracts embedded in the blockchain enable automated, real-time responses to threats, such as isolating compromised nodes or rerouting energy flows to maintain grid stability. Through simulations replicating real-world cyberattacks, the proposed framework demonstrated over 95% detection accuracy, a 30% reduction in response times, and enhanced computational efficiency with lower energy consumption. These results affirm the effectiveness of the framework as a scalable and resilient solution for modern smart grid security.
In light of the escalating complexity of the cyber threat environment, the role of Collaborative Intrusion Detection Systems (CIDSs) in reinforcing contemporary cybersecurity defenses is becoming ever more critical. This paper presents a Blockchain-based Collaborative Intrusion Detection Framework (BCIDF), an innovative methodology aimed at enhancing the efficacy of threat detection and information dissemination. To address the issue of alert collisions during data exchange, an Alternating Random Assignment Selection Mechanism (ARASM) is proposed. This mechanism aims to optimize the selection process of domain leader nodes, thereby partitioning traffic and reducing the size of conflict domains. Unlike conventional CIDS approaches that typically rely on independent node-level detection, our framework incorporates a Weighted Random Forest (WRF) ensemble learning algorithm, enabling collaborative detection among nodes and significantly boosting the system’s overall detection capability. The viability of the BCIDF framework has been rigorously assessed through extensive experimentation utilizing the NSL-KDD dataset. The empirical findings indicate that BCIDF outperforms traditional intrusion detection systems in terms of detection precision, offering a robust and highly effective solution within the realm of cybersecurity.
Abdullah Umar, Sumit Kumar Jha, Deepak Kumar, T. K. Ghose · 5 authors
In isolated microgrids, distributed energy resources (DERs) such as small-scale generators, energy storage systems, and flexible loads operate independently from the main grid. The challenge is to optimize these resources to minimize user costs while ensuring microgrid stability and efficiency. This paper presents an optimization framework for DERs, leveraging a game-theoretical approach to demand-side management (DSM) in an isolated microgrid environment. Each participant aims to minimize their total cost by strategically managing renewable energy generation, storage, and consumption. The framework models the DSM problem as a noncooperative game, identifying equilibrium points where no user can unilaterally reduce costs. The proximal decomposition algorithm is employed to iteratively update user strategies, ensuring convergence to a Nash equilibrium. Furthermore, a blockchain-based system with smart contracts is integrated to automate critical processes, including registration, event detection, DSM actions, and incentive distribution. This integration enhances transparency, security, and efficiency in the microgrid. During the registration phase, all devices are authenticated and authorized through a secure, transparent blockchain ledger. Event detection is managed by the microgrid Energy Management System (EMS), which continuously monitors voltage and frequency levels, triggering predefined smart contract responses to maintain stability. DSM actions are automatically executed by smart contracts, adjusting energy loads, generation, and storage to balance supply and demand dynamically. The smart contracts also manage the economic incentives that drive participant engagement. They calculate and distribute incentives based on predefined criteria, ensuring accurate and prompt allocation. This process is recorded on the blockchain, providing an immutable and auditable trail of actions and rewards. By leveraging blockchain technology and a game-theoretical approach, the proposed framework ensures continuous optimal operation despite fluctuations in energy demand and renewable generation. This dynamic and adaptive model promotes decentralized and efficient energy management within the microgrid, fostering a resilient and sustainable energy ecosystem.
Industry 4.0 has accelerated the adoption of the Industrial Internet of Things (IIoT), enabling intelligent communication among industrial devices, edge systems, and cloud platforms for smart manufacturing. However, conventional centralized security approaches are increasingly vulnerable to cyber threats, data tampering, and single-point failures. This paper proposes a secure blockchain-enabled IIoT architecture that integrates industrial sensing, edge computing, distributed ledger technology, cloud analytics, and intelligent decision-making. The framework employs device authentication, encrypted communication, decentralized consensus, smart contracts, and machine learning-based anomaly detection to enhance data integrity, secure information sharing, and cyber resilience. Experimental evaluation demonstrates improvements in communication security, authentication accuracy, transparency, scalability, latency, and throughput, making the proposed architecture a robust and scalable solution for secure next-generation smart engineering and industrial automation.
This paper introduces a novel framework for Zero-Knowledge Infrastructure Verification (ZKIV) that combines chaos engineering principles with security operations and zero-knowledge proofs to create a robust infrastructure verification system. By leveraging these technologies within a DevOps context, organizations can validate the integrity and security posture of their infrastructure without revealing sensitive configuration details or credentials. This approach, which we term ChaosSecOps, represents a significant advancement in infrastructure security verification, enabling teams to verify compliance, detect misconfigurations, and identify vulnerabilities without exposing sensitive information. Through a detailed AWS implementation case study, this paper demonstrates how ZKIV can be applied to modern cloud environments to enhance security, streamline compliance verification, and build resilient systems.Executive SummaryThis paper introduces Zero-Knowledge Infrastructure Verification (ZKIV), a novel framework for validating the security and compliance of complex, modern infrastructure (particularly cloud environments like AWS) without exposing sensitive configuration details or credentials. ZKIV achieves this by combining principles from:• Zero-Knowledge Proofs (ZKPs): While full cryptographic ZKPs are discussed, the paper focuses on "functional zero-knowledge" approaches practical for infrastructure. This means proving that security controls are in place and functioning correctly without revealing the underlying configurations themselves. Examples include black-box testing, output-only verification, and attestation.• Chaos Engineering: The deliberate introduction of controlled failures (like misconfigurations or simulated attacks) to test system resilience and the effectiveness of security controls.• Security Operations (SecOps): Continuous monitoring, threat response, and security automation practices.• DevOps: Leveraging automation, continuous integration/continuous delivery (CI/CD), and Infrastructure as Code (IaC). The integration of these disciplines is termed ChaosSecOps. Key Benefits of ZKIV• Enhanced Security: Verification happens without needing to expose sensitive data, reducing the attack surface.• Improved Compliance: Continuous, automated verification ensures ongoing adherence to regulatory and internal security policies (e.g., PCI DSS, SOC 2). Evidence is collected in a zero-knowledge manner.• Reduced Operation Risk: Proactive identification of vulnerabilities and misconfigurations before they can be exploited.• Increased Confidence: Greater assurance in the security posture due to systematic and continuous testing.• Scalability: Verification is automated and can be used across many systems.• Efficiency: Verification can be done faster.ZKIV Framework ComponentsThe framework consists of several key components that work together:• Verification Orchestrator: The central control point for scheduling, executing, and managing verification tests.• Policy Engine: Defines and enforces security and compliance rules (using policy-as-code).• Test Agents: Ephemeral (short-lived) components deployed within the infrastructure to perform black-box testing.• Evidence Collection System: Gathers test results in a way that preserves zero-knowledge (no sensitive data revealed).• Remediation Framework: Automates the fixing of identified security issues.AWS Implementation Case StudyA detailed case study demonstrates ZKIV implementation within a financial services organization using AWS. Key AWS services used include AWS Organizations, Security Hub, Lambda, Step Functions, EventBridge, Systems Manager, S3, and Config. The case study shows practical application of zero-knowledge techniques like:• Least-Privilege IAM Roles: Verification agents have only the permissions needed to check configurations, not to access the data they protect.• Output-Only Verification: Validating database security settings without querying the database itself.• Black-Box Network Testing: Using isolated containers to test network segmentation without accessing internal network configurations.
This paper presents a novel blockchain-embedded cybersecurity framework for industrial solar power systems, integrating immutable machine learning (ML) with distributed ledger technology. Our contribution focused on three fac... | Find, read and cite all the research you need on Tech Science Press
Industrial Cyber-Physical Systems (ICPSs) play a vital role in modern industries by providing an intellectual foundation for automated operations. With the increasing integration of information-driven processes, ensuring the ... | Find, read and cite all the research you need on Tech Science Press
Jan 1, 2025·VI All-Russian (National) Scientific Conference "Science, Technology, Society: Ecological Engineering for Sustainable Development of Territories"
A mathematical approach is proposed for evaluating the resilience of a distributed ledger of SmartDust-class sensor microsystems under conditions of cyber-physical attacks and industrial interference. A stochastic model of the trusted sensor network is developed, incorporating the probabilities of node compromise, telemetry packet loss, and data verification errors. Analytical expressions are derived for the indicators of data trustworthiness, latency, and energy reliability of the system. The simulation results demonstrate the influence of network topology parameters and attack intensity on the integral resilience index of the sensor infrastructure.