Blockchain systems, such as Bitcoin and Ethereum 2.0, face vulnerabilities under bandwidth-constrained partitions, where throughput collapses and latency increases. In addition, adversaries can exploit inconsistencies to launch double-spending attacks. This study presents a lightweight dual-layer countermeasure that integrates a robust freezing threshold ( ) with multi-signal disconnection proofs to enhance performance and security without altering consensus rules. Controlled simulation experiments on Bitcoin (PoW) and Ethereum 2.0 (PoS) show throughput gains exceeding 1000% in Ethereum and over 100% in Bitcoin, with inconsistency reduced by up to 64% and latency bounded within 5-6 blocks/s. These results confirm that attacker-aware thresholds and multi-signal validation substantially improve blockchain resilience under partitioned network conditions.
Reaching consensus in Proof-of-Stake (PoS) based consensus protocols, requires supermajority agreement among participating validator nodes. Such protocols need significant network resources due to the concurrent voting of a large number of consensus nodes. As a solution, these nodes are divided into committees, with each committee voting individually at a dedicated time slot. In this paper, we introduce CliqueSensus, a protocol that, given a distribution of consensus nodes into committees, lets them self-organize into small, ephemeral clusters structured in clique topologies, to accelerate the voting process, while using only a small fraction of the network resources required by conventional message dissemination methods. Our evaluation demonstrates that our protocol exhibits rapid convergence and operates with minimal network overhead. We focus on the PoS consensus algorithm adopted by Ethereum 2.0. In addition to our protocol, we also analyze and simulate the clustering approach that Ethereum has adopted, showcasing that our protocol can reduce validation message dissemination time by 23% to 70%, while requiring about 190 times fewer validation message forwards.
The Proof of Stake (PoS) consensus mechanism is increasingly used in blockchain systems; however, resource allocation for PoS-based mobile blockchain networks remains underexplored, particularly given the constraints of mobile devices. This work introduces MEC-Chain, a new framework that integrates Mobile Edge Computing (MEC) with mobile blockchain to support efficient validator-node execution under PoS. MEC-Chain formalizes a multi-objective resource-allocation problem that jointly considers latency, reliability, and cost from both the validator and MEC-provider perspectives. To address this challenge, we develop a deep reinforcement learning-based allocation agent using the Proximal Policy Optimization (PPO) algorithm. Experimental results show that PPO achieves a 30â40% reduction in total execution time, 25â35% lower transmission latency, and 10â15% higher reliability compared to A2C (Advantage ActorâCritic) and DQN (Deep Q-Network), while offering comparable cost savings across all methods. These results demonstrate the effectiveness of MEC-Chain in enabling low-latency, reliable, and resource-efficient PoS validation within mobile blockchain environments.
Blockchain networks face a critical but understudied threat: wave attacks that exploit difficulty adjustment algorithms through strategic mining participation. Adversaries cyclically withdraw and re-enter mining to create oscillations that degrade network liveness and destabilize honest minersâ revenue. We present the first production-ready framework that maintains network responsiveness while enabling robust, post hoc threat detection. The framework employs a statistically rigorous pipeline featuring controller-aligned anomaly detection, transitive collusion grouping via union-find, and BenjaminiâHochberg False Discovery Rate control. We formally prove the economic viability of this architecture: when penalties on unvested rewards are enabled by governance, wave attacks become asymptotically unprofitable for rational adversaries. Evaluated on a 128-node distributed testbed simulating Bitcoin, Ethereum Classic, and Monacoin networks over 30 independent runs, our framework achieves 92.7% F1-score in detecting attacks, significantly outperforming baseline methods (74.7%). This work provides a complete, theoretically-grounded solution for securing proof-of-work blockchains against difficulty manipulation, forming the foundation for the adaptive AI-driven enhancements presented in our companion paper (Part II).
Decentralized multi-agent systems have shown promise in enabling autonomous collaboration among LLM-based agents. While AgentNet demonstrated the feasibility of fully decentralized coordination through dynamic DAG topologies, several limitations remain: scalability challenges with large agent populations, communication overhead, lack of privacy guarantees, and suboptimal resource allocation. We propose AgentNet++, a hierarchical decentralized framework that extends AgentNet with multilevel agent organization, privacy-preserving knowledge sharing via differential privacy and secure aggregation, adaptive resource management, and theoretical convergence guarantees. Our approach introduces cluster-based hierarchies where agents self-organize into specialized groups, enabling efficient task routing and knowledge distillation while maintaining full decentralization. We provide formal analysis of convergence properties and privacy bounds, and demonstrate through extensive experiments on complex multi-agent tasks that AgentNet++ achieves 23% higher task completion rates, 40% reduction in communication overhead, and maintains strong privacy guarantees compared to AgentNet and other baselines. Our framework scales effectively to 1000+ agents while preserving the emergent intelligence properties of the original AgentNet.
Decentralized Physical Infrastructure Networks (DePIN) represent an emerging organizational form for operating physical infrastructure through blockchain-based coordination. DePIN through decentralized protocols and token-based payment mechanisms incentivize independent agents to deploy, maintain, and monetize real-world infrastructure, such as wireless networks, storage units, or sensors. This article presents a first formal economic analysis of DePIN architectures, modelling investment decisions under network effects in a blockchain-native Decentralized Autonomous Organization (DAO), with protocol-defined reward schemes. It establishes the equilibrium conditions that support decentralized provision, where token prices internalize participation, service reliability, and network coverage. Furthermore, it identifies a minimum viable coverage threshold determined by costs and network effects. Through a multi-agent machine learning simulation, we confirm that decentralized provision improves efficiency compared to centralized models. The results support the economic viability of DePIN and provide design guidelines for future decentralized infrastructure protocols. Finally, we propose an DAO incentive mechanism to implement First Best provision in Decentralized Physical Infrastructure Networks.
Dynamic spectrum sharing (DSS) is essential for 6G networks, yet existing blockchain-based DSS solutions often lack an integrated approach that simultaneously addresses trust, allocation fairness, and system scalability. This paper proposes HierSpectrumChain, a hierarchical blockchain framework that incorporates a global main chain, localized sub-chains, and a smart-contract based Stackelberg auction for credible and automated spectrum allocation. The system model formalizes interactions among spectrum holders, secondary users, and sub-chain validators, enabling transparent bidding and decentralized coordination. A proof-of-concept implementation on an Ethereum Ganache environment evaluates the functional correctness of the auction workflow and measures throughput under varying client loads. While the evaluation is limited to a single-node testbed, the results demonstrate the feasibility of the proposed architecture and establish a basis for future multi-peer experiments on permissioned blockchains. This work provides a coherent design and initial validation for blockchain-enabled DSS in 6G networks.
Sasikumar Asaithambi, Sunil Prajapat, Mohammed Wasim Bhatt, Syed Rizwan Hassan
ABSTRACT The Internet of Things (IoT) can offer more precise, intelligent, and lowâ or nonâhuman involvement approaches to various sectors. One of the significant uses of the IoT is in smart cities, which includes a variety of services, including smart home, garbage disposal, and smart grid. Many different collaborative IoT integrations are available in smart cities because of these diverse services. A digitized smart city was created to offer complete government cooperation solutions based on digitization and automation to improve residents' quality of life. Information safety and privacy concerns arise when various services need to work together seamlessly. Trustworthy data is vital to the federal government and its constituents, and data accuracy and privacy must be ensured. In this work, we presented a smart contractâenabled smart city and softwareâdefined networking (SDN) in limited contexts during collaborative activities based on a controlled network and decentralization. The proposed collaborative application safety structure is being tested on the Hyperledger blockchain networks. We describe a unique approach to data security through collaborative work in intelligent city governmental design, utilizing ProofâofâTrust Collaboration (PoTC) in Hyperledger blockchains. A security approach based on SDN and smart contracts is employed to safely manage and monitor all connections and transactions across diverse IoT networks. To assess the viability of the proposed decentralized security framework, we created a supported scenario for collaborative activities in an SDNâenabled IoT design. We have conducted various experimental simulations to test the proposed blockchainâintegrated SDNâbased IoT architecture for a smart city, including throughput, access delay, and trust evaluation. The simulation results show that the proposed SDNâenabled blockchain networks provide better results than existing works.
Verifiable network telemetry is crucial for ensuring transparency and trust in network measurements. However, telemetry logs (e.g., NetFlow records) often contain sensitive data, making public verification challenging. Recent work has attempted to address this problem using Trusted Execution Environments (TEEs), such as Intel SGX, to provide confidentiality and integrity guarantees. However, TEEs are known to suffer from complex deployment requirements and limited scalability. In this paper, we introduce a software-based approach utilizing the latest advances in Zero-knowledge Proofs (ZKPs) to enable verifiable network telemetry without revealing the underlying sensitive logs or relying on special-purpose hardware. Our system employs a general-purpose ZKP virtual machine (RISC Zero) to generate cryptographic proofs over NetFlow data, enabling operators to securely attest to network flow metrics. Our preliminary results indicate that our ZKP-based design offers a viable path toward overcoming deployment and scalability limitations inherent in the solutions that require special-purpose hardware.
Abstract Distributed Software Define Network (DSDN) offers to eliminate the single point of failure present in the centralized SDN controller. The main concept is to have numerous controllers that can share the burden on the network, and one controller can take over another controller when it breaks. In this study, we work on an Opendaylight-based completely distributed system to deal with issues of the controller's central processing unit (CPU) consumption being uneven. Every controller administers its own domain therefore sharing the burden across the network's controllers. To update the flow rules and broadcast new rules to all the controllers at once, maintaining the consistency of the controller's rule set, we create an application ledger that maintains a distributed ledger. Results demonstrate that, in addition to ensuring the uniformity of inventory shards across all controllers in the system, our work presented an increase with throughput reaching 40% and the bandwidth of controller's communication is less with almost 31%.
Collin Arnold Kabwama, Osorachukwu Maurice Ayozie, Justin Njimgou Zeyeum, Adeniran Oluwatoyosi Awe · 6 authors
As Critical National Infrastructure (CNI) becomes increasingly digitized, traditional reactive security assessments are failing to keep pace with automated threats. This paper proposes an autonomous framework integrating Generative Adversarial Networks (GANs) and Distributed Ledger Technology (DLT) for proactive vulnerability discovery and immutable infrastructure hardening. We utilize a Physics-Aware Wasserstein GAN to synthesize protocol-specific attack vectors that identify "zero-day" weaknesses in Industrial Control Systems (ICS) by exploring the operational state space of protocols like DNP3 and Modbus. Discovered vulnerabilities are committed to an Immutable Knowledge Base (IKB) on a sharded, permissioned blockchain, providing a "Single Source of Truth" for cross-sector threat intelligence. To automate mitigation, Smart Contracts orchestrate infrastructure hardening by validating patches through digital twin simulations before network-wide deployment. Experimental results using HELICS and NS-3 demonstrate that this architecture reduces the Mean Time to Remediate (MTTR) from days to sub-second intervals. Finally, we address long-term security by incorporating Post-Quantum Cryptography (PQC) to protect the ledger against emerging quantum threats.
Open access
Smart Grid Security and Resilience
Software-Defined Networks and 5G
Infrastructure Resilience and Vulnerability Analysis
Chung, Siu Kei, Francisco Carpio, Andrei Navoichyk, Siarhei Valasovich · 11 authors
The internet faces a sovereignty crisis due to power concentration and data growth among a few hyperscalers, leading to centralization and loss of user control. This consolidation risks censorship and creates single points of failure. While Web3 offers decentralized solutions, they often sacrifice either scalability, decentralization, or security, which are key elements in the blockchain trilemma. These solutions also struggle with limited access to enterprise-grade hardware and frequently rely on centralized infrastructure. The Impossible Cloud Network (ICN) addresses these issues by creating a multi-tiered, decentralized infrastructure layer. ICN offers a composable service layer, an enterprise-grade hardware resource layer, and a transparent, permissionless HyperNode network for performance enforcement. By strategically decoupling and decentralizing each layer, ICN aims to provide an open, extensively scalable infrastructure that ensures digital sovereignty, eliminates single points of trust, enables service programmability, and offers a decoupled architecture for limitless possibilities in the future internet.
Ivan Homoliak, Martin PereĆĄĂni, Marek TamaĆĄkoviÄ, Timotej Ponek · 6 authors
Proof-of-Stake (PoS) consensus protocols often face a trade-off between performance and security. Protocols that preelect leaders for subsequent rounds are vulnerable to Denial-of-Service (DoS) attacks, which can disrupt the network and compromise liveness. In this work, we present PoS-CoPOR, a single-chain PoS consensus protocol that mitigates this vulnerability by integrating a native onion routing mechanism into the consensus protocol itself. PoS-CoPOR combines stake-weighted probabilistic leader election with an anonymization layer that conceals the network identity of the next block proposer. This approach prevents targeted DoS attacks on leaders before they produce a block, thus enhancing network resilience. We implemented and evaluated PoS-CoPOR, demonstrating its ability to achieve a throughput of up to $110 \mathrm{tx} / \mathrm{s}$ with 6 nodes, even with the overhead of the anonymization layer. The results show that native anonymization can provide robust DoS resistance with only a modest impact on performance, offering a solution to build secure and scalable PoS blockchains.
The Internet of Things (IoT) has made secure and reliable data communication more difficult due to its dynamic topologies, energy constrictions, and intelligent and sophisticated adversaries.To address these difficulties in IoT networks, we propose G-TrustChain, an integrated hybrid framework based on Graph Neural Networks (GNNs) for intelligent and dynamic routing and a light Blockchain for distributed trust.G-TrustChain makes use of node-level parameters including latency, remaining energy, and behavioural trust scores derived from a Graph Attention Network (GAT) for routing paths.A lightweight Directed Acyclic Graph (DAG)-structure Blockchain maintains trust scores with a distributed, scalable, and tamper-proof ledger that minimizes dependency on a centralized authority.Experimentation is done for 10,000 rounds, G-TrustChain demonstrated superior routing performance to other protocols such as Trust-based Routing, BBTR, and ROUTENET.It is achieving 95.6% packet delivery ratio, 91.2% detection rate of attacks, and energy consumption as low as 0.0110 J/bit.Also achieving more accurate and reliable trust scores despite energy constraints and higher/extensive attacks.These outcomes demonstrated G-TrustChain provides energy-efficient, secure, and intelligent data communication for the next generation of IoT networks.
Software-Defined Networking (SDN) has revolutionized network administration with its unparalleled flexibility and programmability. The secure placement of controllers within a Software-Defined Networking framework remains a significant challenge. This research provides an innovative solution that integrates blockchain technology with the advanced reinforcement learning algorithm MuZero to optimize and secure the placement of SDN controllers. The suggested framework utilizes critical security parameters, including network latency, traffic volume, and the quantity of connected devices, to evaluate and record secure controller locations, employing Mininet for network emulation and OpenDaylight as the SDN controller. These criteria are essential for identifying secure deployment locations and assessing network efficacy. The suggested solution ensures the security and efficacy of controller placement in SDN systems by integrating blockchain technology for transparent and invulnerable documentation of secure locations.
Ethereumâs transaction pool (mempool) dynamics and fee market efficiency critically affect transaction inclusion, validator workload, and overall network performance. This research empirically analyzes gas price variations, mempool clearance rates, and block finalization times in Ethereumâs proof-of-stake ecosystem using real-time data from Geth and Prysm nodes. We observe that high-fee transactions are consistently prioritized, while low-fee transactions face delays or exclusionâdespite EIP-1559âs intended improvements. Mempool congestion remains a key factor in validator efficiency and proposal latency. We provide empirical evidence of persistent fee-based disparities and show that extremely high fees do not always guarantee faster confirmation, revealing inefficiencies in the current fee market. To address these issues, we propose congestion-aware fee adjustments, reserved block slots for low-fee transactions, and improved handling of out-of-gas vulnerabilities. By mitigating prioritization bias and execution inefficiencies, our findings support more equitable transaction inclusion, enhance validator performance, and promote scalability. This work contributes to Ethereumâs long-term decentralization by reducing dependence on high transaction fees for network participation.
Software defined networking (SDN) increasingly integrates multiple controllers from diverse vendors to enhance network scalability, flexibility, and reliability. However, such heterogeneous deployments pose significant security threats, especially at the east-west interface which is connecting these controllers. Existing solutions are inadequate for ensuring robust protection across multi-vendor SDN environments as most of them are meant to a specific type of attacks, use centralized solution, or designed for homogeneous SDN environments. This study proposes a blockchain-based security framework to address existing security gaps within heterogeneous SDN environments. The framework establishes a decentralized, robust, and interoperable security layer for distributed SDN controllers. By utilizing the Ethereum blockchain with customized smart contract-based checks, the proposed approach enables mutual authentication among controllers, secures data exchange, and controls network access. The framework effectively mitigates common SDN threats such as distributed denial-of-service (DDoS), man-in-the-middle (MitM), false data injection, and unauthorized access. Experimental results highlight the practicality of the solution, achieving a stable throughput of approximately 20 transactions per second with an average authentication latency of 28-40 ms. These results demonstrate that the proposed framework not only enhances inter-controller communication security but also maintains the network performance, making it a reliable and scalable solution for real-world SDN deployments.
ABSTRACT The emergence of wireless technology brought about enhanced communication across various devices, resulting in the demand for efficient and reliable wireless networks, like wireless mesh networks (WMNs) and mobile Adâhoc Networks (MANETs). MANETs are known for their decentralized nature, rapid deployment, infrastructureâless operation, adaptability, and ease of use in several applications and outdoor events. Despite their flexibility, they often face challenges relating to security vulnerabilities, together with blackhole and grayhole attacks, and tradeâoffs in terms of performance relating to reliability and integrity. This paper proposes an improved, innovative routing protocol for Adâhoc OnâDemand Distance Vector (AODV) by infusion of blockchain's proof of stake (PoS) consensus mechanism named PoSAODV, whose objective is to enhance security, energyâefficiency, and adaptability while reducing packet loss rate, routing overheads, and increasing throughput. Smart contractâbased validator selection was utilized to ensure fairness and reduce blackhole and grayhole attacks. The result obtained through simulation demonstrates that PoSAODV outperforms the original AODV by reduced latency of 0.79 ms , average throughput of 45 Mbps , and packet delivery ratio of 80%â100% in both unsafe and safe environments. This makes PoSAODV suitable for resourceâconstrained adâhoc networks with dynamic topologies.
Hina Binte Haq, Syed Taha Ali, A. G. Sal'Man, Patrick McCorry · 5 authors
The Bitcoin mempool plays an integral role in transaction processing and propagation through the network. Frequent transaction congestion events, as well as spam and dust attacks can clog the mempool, leading to dropped transactions, processing delays, and increased transaction fees. Moreover, increasing transaction loads on the network result in higher resource costs to operate full nodes, thereby restricting Bitcoin's network footprint and negatively impacting its overall health and performance. In this paper, we present Carbyne, a novel mempool optimization scheme, which uses counting bloom filter constructions to adapt to increased transaction flows, thereby making nodes resilient to congestion and spam and dust attacks. We implement Carbyne in C++ and benchmark its performance using a novel data set of Bitcoin mempool activity over a 90-day period. We dramatically reduced the mempool's memory consumption by up to two orders of magnitude (from 300 MB to 3 MB) while verifying and forwarding transactions with 99.9% fidelity and a slight increase in computational load. We simulate extensive spam attacks on Carbyne and demonstrate that mempool loads of 1 GB can be accommodated in as little as 10 MB. Carbyne does not necessitate a hard fork, it will help deploy high-functioning nodes on resource-constrained platforms, and it may also be adapted to other cryptocurrencies.
Prof. R. C. Pachhade, Shubham Gaikwad, Aditya Sawwase, Dahihande Rohan
The idea focuses on enhancing the security and reliability of data exchange between military units. Traditional methods of secure communication often involve centralized systems, which can be vulnerable to breaches and single points of failure. By utilizing blockchain technology, the implementing idea introduces a decentralized approach that ensures data integrity and security through a distributed ledger system. In this system, blockchain provides a tamper-proof record of all communications, ensuring that data is encrypted, verified, and resistant to unauthorized access. This decentralized model eliminates the need for a central authority, reducing potential vulnerabilities and increasing the resilience of the communication network. As a result, the system aims to offer a more secure, reliable, and robust solution for confidential data transmission between army stations, enhancing operational security and efficiency.
Federated Learning (FL) is a decentralized learning method that enables collaborative model training while preserving data privacy. This makes FL a promising solution in various applications, particularly in cross-silo settings such as healthcare, finance, and transportation. However, FL remains highly vulnerable to adversarial threats, especially backdoor attacks, where malicious clients inject poisoned data to manipulate global model behavior. Existing outlier detection techniques often struggle to effectively isolate such adversarial updates, compromising model integrity. To address this challenge, we propose Backdoor Attack Resilient Technique for Federated Learning (BART-FL), a novel lightweight defense mechanism that enhances FL security through malicious client filtering. Our method integrates Principal Component Analysis (PCA) for dimensionality reduction with cosine similarity for measuring pairwise distances between model updates andK-means clustering for detecting potentially malicious clients. To reliably identify the benign cluster, we introduce a multi-metric statistical voting mechanism based on point-level mean, median absolute deviation (MAD), and cluster-level mean. This approach strengthens model resilience against adversarial manipulations by identifying and filtering malicious updates before aggregation, thereby preserving the integrity of the global model. Experimental evaluations conducted on the LISA traffic light dataset, CIFAR-10, and CIFAR-100 demonstrate the effectiveness of BART-FL in maintaining model performance across diverse FL settings. Additionally, we perform a comparative analysis against existing backdoor defense techniques, highlighting BART-FLâs ability to improve security while ensuring computational efficiency. Our results showcase the potential of BART-FL as a scalable and adversary-resilient defense mechanism for secure training in cross-silo FL applications.
In multi-domain network environments, each domain operates with its own Software-Defined Networking (SDN) controller, making it challenging to ensure secure and consistent topology management across domains. Traditional centralized logging systems fall short in providing the transparency, flexibility, and decentralized trust needed for effective collaboration between controllers. Distributed Ledger Technologies (DLT) offer a decentralized and immutable way to log network operations, presenting a promising solution for multi-domain SDN environments. This paper presents an approach that uses blockchain to record topology management operations such as adding, updating, and deleting devices and links across multiple SDN domains. The framework leverages permissioned blockchains to establish decentralized trust, with SDN controllers acting as validator nodes to validate and commit transactions upon reaching consensus. An Adaptive Batch Mechanism (ABM) is introduced to optimize block formation by dynamically adjusting batch parameters and using dummy transactions to maintain efficiency during low and varying transaction loads. Experimental validation using Hyperledger Fabric demonstrates the systemâs ability to enhance decentralized logging, reduce delays in transaction processing, and facilitate inter-domain coordination while maintaining robust security through a distributed trust model.
Zubaida Rehman, Iqbal Gondal, Hai Dong, Mengmeng Ge · 6 authors
Eclipse attacks, which isolate victim nodes by monopolizing their peer connections, remain a critical threat to Ethereumâs consensus mechanism. To address this, we present a principled framework for detecting Eclipse attacks in Ethereum peer-to-peer networks, grounded in a formal adversarial model. Existing defenses are either ad-hoc or lack provable guarantees, leaving open questions about their reliability under adaptive adversaries. Our work aims to bridge this gap by formally defining eclipse attack detection as a security property. We specify soundness, completeness, and robustness theorems under bounded adversarial drift, and derive formal guarantees within false positive and false negative bounds, resilience to adversarial manipulation, and multi-node compositional reliability. We then instantiate a lightweight detection framework that maps packet-level traffic features to predictions using ensemble classifiers (Random Forest, XGBoost). The system was validated using a controlled Ethereum testbed and extended with CTGAN-generated synthetic traces to emulate networks of up to 100 nodes. Empirical evaluation shows that our framework achieves up to 96% F1-score with sub-second inference latency, well within Ethereumâs 12-second Proof-of-Stake validator time slots. These findings demonstrate that lightweight statistical features, when coupled with formal analysis, enable accurate, efficient, and scalable detection of network-level partitioning attacks. Our work establishes a deployable and theoretically grounded defense foundation for securing modern blockchain systems against eclipse adversaries.
Web3 applications, such as on-chain gaming, require unbiased and publicly verifiable randomness that can be obtained quickly and cost-effectively whenever needed. Existing services, such as those based on Verifiable Random Functions (VRF), incur network delays and high fees due to their highly interactive nature. FlexiRand [CCS 2023] addressed these problems by hiding the output of the VRF and using that as a seed to derive many randomnesses locally. These randomnesses are instantly available for usage. However, these randomnesses can not be verified independently (or instantly) without disclosing the seed, leaving scope for malicious actors to cheat. To solve this problem, we introduce a new notion, called instantly-verifiable VRF (iVRF), which enables the generation of many randomnesses from one VRF output seed, such that each of them is verifiable independently - this enables the solution to generate randomnesses, such that they are and also . To instantiate we propose a generic construction called InstaRand - it combines any (possibly distributed) VRF at the server's end with another VRF at the client's end to construct an iVRF. Our specific instantiation uses the BLS-based GLOW-DVRF [Euro S&P 2021] at the server's end and the DDH-based VRF of Goldberg et al. [RFC 2023] at the client's end. We use the universal composability framework to analyze the security. Moreover, due to its generality, InstaRand can be instantiated with any post-quantum secure VRF to yield a post-quantum secure iVRF. Our experiments demonstrate that our instantiation of InstaRand is . The client incurs a cost to generate the seed (server's VRF output) by querying the GLOW-dVRF servers once. Once the seed is set up, the client locally generates the pseudorandom value on demand in , avoiding the client-server round-trip delay. Each value can be independently verified in . This yields a improvement in terms of output generation and improvement in verification cost over existing solutions.