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 Perešíni, Marek Tamaškovič, 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.
Emerging applications need dynamic, deterministic performance across network and cloud providers. We demonstrate a blockchain-based solution with smart contract execution to establish network and cloud services on a multi-provider, multi-layer IP-optical network and cloud infrastructure.
The rise of non-fungible tokens (NFTs) has moved beyond digital art into decentralized finance (DeFi) for loans. NFTs are now commonly used as collateral in many decentralized lending platforms in DeFi. The paper evaluates three lending protocols: NFTfi, Arcade, and BendDAO in detail. These platforms show different methods for risk mitigation, liquidation, and borrower-lender interactions. This paper suggests a detailed risk assessment framework for NFT collateral in DeFi lending platforms. It highlights NFT illiquidity, high volatility, valuation uncertainty, and protocol design problems. The use of Layer -2 blockchain based lending model results in reduced gas fees and supports scalability. It is specifically optimized for networks like Polygon that offer higher throughput and lower operational costs. Layer-2 deployment facilitates faster processing speeds and significantly reduces transaction fees for users. The framework integrates interoperable blockchain protocols that support seamless NFT collateral migration across chains. This cross-chain operability increases platform liquidity and lending flexibility for decentralized finance participants. Dynamic, risk-aware smart contracts modify lending terms in real time using asset volatility indicators. They assess borrower risk profiles with on-chain data to maintain adaptive, secure lending conditions. Monte Carlo simulation is used to estimate default risks and delays in NFT liquidation. This simulation helps understand results when the market conditions keep changing. Findings say lending platforms need dynamic risk settings and strong pricing oracles for safety. Smart, borrower behavior-based lending strategies are also needed for steady growth in NFT backed DeFi lending. This paper also provides insights into use of advance techniques beyond smart contracts to enhance the system. This research gives useful ideas to DeFi developers, investors, and regulators handling NFT collateral. It helps people understand NFT lending risks better and enable us to design stronger lending systems.
Damla Sarıçelik, Mohaned Chraiti, Albert Lévi, Özgür Erçetin
The Open Radio Access Network (O-RAN) paradigm fosters multi-vendor interoperability, allowing modules from different vendors to cooperatively handle network functions, such as temporary data processing or sensor data collection for network operations optimization. However, this integration agility introduces the risk of selecting suboptimal or adversarial modules, leading to moral hazard. Traditional Moral Hazard testing approaches typically rely on a benchmarking data set in addition to historical performance score. However, they deemed impractical, as vendor-supplied modules may not reveal their outputs before deployment, and the network may lack direct access to reference results for validation. This challenge is further compounded by the dynamic nature of network elements and AI-driven models, whose performance can degrade over time due to malicious tampering, obsolescence, or device deterioration, making historical quality assessments ineffective. In this paper, we address the challenge of identifying legitimate vendor-supplied modules among adversarial ones, with respect to a given network functionality/operation, in the absence of benchmarks. We propose a benchmark-free test framework that detects and eliminates adversarial modules using a methodology inspired by the WereWolf game, combined with zero-knowledge proof techniques. Monte Carlo simulations demonstrate that our approach effectively removes adversarial entities while preserving the privacy of legitimate modules.
Ketav Shah, Aparna Kumari, Bhavya Solanki, S. N. Saud · 5 authors
This research presents ZTSec-FedSDN, a privacy-preserving federated framework for SDN attack detection that integrates zero-trust blockchain architecture with 6G terahertz networks. The framework enables collaborative training of Deep Neural Network models across distributed clients while maintaining data privacy and leveraging the high-speed capabilities of 6G terahertz communication. Using the SDNFlow dataset containing diverse network traffic patterns and attack types, we implement four federated optimization strategies: Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Adam (FedAdam), and Federated Adagrad (FedAdagrad). The zero-trust blockchain layer provides an immutable ledger for recording and verifying model updates, ensuring transparency and trustworthiness in the federated learning process. The integration with 6G terahertz networks enables ultra-low latency communication between federated clients, crucial for real-time intrusion detection in SDN environments. Multiple clients collaboratively train the shared DNN model on local traffic data without exposing sensitive information, preserving data privacy while benefiting from collective intelligence. This work systematically evaluates the performance of different federated learning algorithms by comparing model accuracy, convergence speed, robustness, and efficacy in multi-class network attack classification. The results provide comprehensive insights into each optimizer’s suitability for secure, transparent, and trustworthy intrusion detection systems in next-generation SDN environments powered by 6G terahertz networks.
Privacy remains a significant challenge in public blockchain ecosystems. Mainstream add-on privacy solutions, such as Stealth Address Protocols (SAPs) and Zero-Knowledge Proof (ZKP)-based mixers, have recently attracted considerable attention. However, existing SAPs offer only ephemeral anonymity for users' transaction data, and their implementation and evaluation within the highly concurrent Unspent Transaction Output (UTXO) model remain largely unexplored. ZKP-based mixers are limited to native coin transfers with fixed denominations and require additional security assumptions, employing out-of-band encrypted channels to transmit notes. To overcome these challenges, we unify the core principles underlying both SAPs and ZKP mixers and formally introduce StealthHub, a UTXObased SAP. Compared with the widely adopted dual-key-based Umbra protocol prevalent on Ethereum Virtual Machine (EVM)-compatible chains, StealthHub reduces computational overhead for the prepare and scan announcements stages by over 71% and 32%, respectively. Furthermore, by leveraging Merkle Mountain Range (MMR) commitments and off-chain batch aggregation, our StealthHub implementation lowers deposit and shielded transfer transaction costs to approximately 76% of those for a standard transfer, substantially improving practical usability.
The rapid expansion of Non-Fungible Tokens (NFTs) ecosystem in Web 3.0 service has produced complex transaction networks that challenge traditional graph analysis methods. To address these limitations, this paper presents a higher-order network framework for modeling and analyzing NFT transaction data. Our approach constructs a multi-layered representation of the NFT ecosystem by integrating temporal hypergraphs and motif-based networks, thereby capturing multiway interactions and temporal dependencies beyond simple pairwise transactions. We target three specific tasks to uncover and leverage latent dependencies in NFT markets. First, we build a temporal hypergraph and motif-based network to encode multientity, multi-token relationships. Next, we develop a Higher-Order Graph Neural Network that incorporates token ownership chains and motif edges to improve node classification, significantly outperforming traditional model in identifying key actor roles. Finally, we propose a link prediction model that integrates shared-token and motif-based features, substantially enhancing accuracy in forecasting new NFT transactions compared to baseline models. Our findings validate that explicitly capturing higher-order dependencies is crucial for robust and interpretable analysis of NFT ecosystems, facilitating better Web 3.0 service.
Ouafae Benoudifa, Abderrahim Ait Wakrime, Rédouane Benaini
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.
Network slicing constitutes a paradigm shift as it transforms a 5G network into a set of versatile sub-networks for designated users, with specific requirements on security levels and quality of service (QoS) demands. Therefore, organizations with very high security and non-negotiable QoS requirements, such as the military, are leveraging the utilization of 5G network slicing for their operations. However, some network management challenges remain before constructing a resilient network with 99.999% reliability to different attacks, while providing isolation, high throughput and low latency. In this respect, we propose a reinforcement learning-based multi-agent resilience framework, which comprises centralized training using global information and decentralized decision-making by individual agents, each corresponding to an access point, to autonomously adapt network slicing configurations based on the evolving tactical landscape. The proposed framework continuously assesses network conditions and threat scenarios, to dynamically allocate the required resources and mitigate vulnerabilities. Numerical results exhibit that our proposed framework effectively defends against different adversarial actions and maintains operational continuity without compromising the QoS.
Ethereum’s 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 Ethereum’s 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 exclusion—despite EIP-1559’s 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 Ethereum’s long-term decentralization by reducing dependence on high transaction fees for network participation.
Cooperation among telecom carriers and datacenter providers (DCPs) is essential to ensure the resiliency of network-cloud ecosystems. To enable efficient cooperative recovery in case of traffic congestion or network failures, we introduce a novel, to our knowledge, multi-entity cooperation platform (MCP) for implementing cooperative recovery planning. The MCP is built over distributed ledger technology (DLT), which ensures decentralized and tamper-proof information exchange among stakeholders to achieve open and fair cooperation. We experimentally demonstrate a proof-of-concept DLT-based MCP on a testbed. We showcase a DCP–carrier cooperative planning process and the corresponding recovery in the data-plane, showing the possibility of multi-entity cooperation for quick recovery of network-cloud ecosystems.
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.
Voting-Based Distributed Ledger Technologies proved efficiency and security in protecting IoT networks. They allow faster approval time, identify malicious information, and isolate adversaries through repetitive queries to adjacent peers asking their opinions about the validity of each transaction. They enable a decentralized scheme that securely constructs and stores all data types, implying either monetary values, system logs, analytical information, or triggered actions. Several consensus algorithms were introduced to enrich distributed IoT networks with data integrity, transparency, resilience, and trust. This work surveys the main challenges for deploying distributed ledgers in IoT environments. It also introduces an adaptive DLT-based framework that standardizes the fundamental required modules for securing communication among heterogeneous IoT devices. The framework comprises four integrated modules that work simultaneously to ensure data integrity and network security. Practical simulation is also performed to evaluate the effectiveness of this framework through a parameterized model. The experimental results conclude that integrating the four functional modules is essential for network efficiency, reliability, and resilience.
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 Ad‐hoc Networks (MANETs). MANETs are known for their decentralized nature, rapid deployment, infrastructure‐less 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 trade‐offs in terms of performance relating to reliability and integrity. This paper proposes an improved, innovative routing protocol for Ad‐hoc On‐Demand Distance Vector (AODV) by infusion of blockchain's proof of stake (PoS) consensus mechanism named PoSAODV, whose objective is to enhance security, energy‐efficiency, and adaptability while reducing packet loss rate, routing overheads, and increasing throughput. Smart contract‐based 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%–100% in both unsafe and safe environments. This makes PoSAODV suitable for resource‐constrained ad‐hoc 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.
With the development and popularization of Internet technology, the issue of network security has become increasingly serious. Network security management has become an urgent problem to be solved. Blockchain technology, as a decentralized, secure, and unalterable distributed ledger technology, is nowadays frequently used in the field of network security management. However, the performance and scalability of blockchain have been bottlenecks in its application. Through an in-depth analysis of the key technologies of blockchain and their application cases in network security management, this chapter summarizes the characteristics, advantages, and limitations of different blockchain technologies used in network security management, and concludes the challenges and problems encountered by blockchain technology in network security management: such as technology maturity, data security, and privacy protection. To solve these problems, a scalable model for managing network security, leveraging blockchain technology to enhance efficiency and trustworthiness is constructed by combining artificial intelligence means such as machine learning and deep learning, and the scalability problems such as low transaction efficiency, high time delay, and low throughput existing in the network security management of blockchain technology are solved, providing new solutions for network security management.
As 6G networks introduce increasingly diverse and complex applications, network slicing is a key enabling technology for partitioning network resources to meet these dynamic demands. However, efficiently managing and allocating these finite resources has become vital. This necessity drives the adoption of an open marketplace model. To address the business and technical complexities associated with such open marketplaces, this paper presents the demonstration of a non-fungible token (NFT)-enabled resource trading marketplace tailored for 6G network slicing. The proposed solution is implemented on an Ethereum-based blockchain system to assess its viability.
The shift from fifth generation (5G) to sixth generation (6G) networks is anticipated to significantly advance network slicing. This progress is driven by the growing demand for next-generation applications and services. However, these advancements must be managed within the constraints of limited resources. This evolution opens up opportunities for resource sharing through emerging marketplaces, yet it introduces various business and technical complexities that need to be addressed. Additionally, finding cost-effective solutions is also essential for the future of networks. In this paper, we propose a blockchain-based architecture that utilizes non-fungible tokens (NFTs) for the trading of network resources within the 6G network slicing. To the best of our knowledge, this is the first study to represent network resources as NFTs within the context of network slicing. The architecture employs NFTs to authenticate and manage various network resources, providing a decentralized platform for their secure creation, management, and exchange. Using resource NFTs, our system ensures more granular and flexible control over network resources than existing state-of-the-art systems, where NFTs are tied to network slices. We implemented a prototype of this system to validate its viability. Our performance evaluation confirms that the proposed approach is efficient and cost-effective compared to baseline models in managing network resources within network slicing. These findings highlight the potential of our system to transform network management practices and effectively meet the demands of future networks.
Payment Channel Networks (PCNs) can significantly enhance the scalability of blockchain transactions without requiring major modifications to the underlying distributed ledger protocol. However, to enable efficient and secure payment routing over multiple channels, PCNs necessitate additional components: an onion routing protocol for anonymity and a locking mechanism to prevent race conditions. Unfortunately, these mechanisms can be exploited by malicious actors to launch flood and loot attacks. We investigate the impact of flood and loot attacks on networks that employ local knowledge-based routing algorithms. In these scenarios, malicious nodes can manipulate hash-locks to unfairly capitalize on the benefits of participating nodes. We specifically examine the effects of these attacks on three popular routing algorithms: Swift, Speedy Murmurs, and Silent Whispers. Our analysis employs metrics like attack gain, attack cost, and transaction ratio to assess the impact of the attacks. Our simulation-based results demonstrate that, even with a relatively small fraction of malicious nodes (between 0.66% and 3.3%), the average attack transaction ratio across all evaluated routing algorithms is a concerning 16.23%.