Mamoon M. Saeed, Rashid A. Saeed, Mohammad Kamrul Hasan, Elmustafa Sayed Ali · 8 authors
After adopting 5G technology, businesses and academia have started working on sixth-generation wireless networking (6G) technologies. Mobile communications options are expected to expand in areas where previous generations could not do so. 6G networks are anticipated to be constructed using various diverse technologies. These encompass diverse cutting-edge advancements, such as distributed ledger systems like blockchain, visible light communications (VLC), post-quantum cryptography, edge computing, molecular communication, THz, and other advances. These advances necessitate a reassessment of previous security strategies from a security perspective. In the future, networks must adhere to stricter criteria for authentication, encryption, access control, connectivity, and detection of harmful activities. Ensuring privacy and dependability necessitates the implementation of supplementary security protocols. The essay explores the primary concerns and challenges related to the security of the 6G network. This paper describes the improvements in security in communications from 1G through 6G. This paper divides security in the sixth generation into three layers: physical, connection, and service. Each layer-by-layer discusses the standard technologies and security issues for each technology proposed in each sixth-generation security layer. All proposed solutions for each of the three layers are discussed in Sixth Generation Security. It also reviews all proposed solutions for each layer, indicating the proposed solution and its limitations.
P. Selvaraj, A. Hyils Sharon Magdalene, Suresh Sankaranarayanan, Alias Muralidharan R. Rengaraj · 7 authors
This work designs a novel algorithm to address the pressing security challenges anticipated in 6G networks. A combination of AES, zero-knowledge proofs, and RSA algorithms offers a robust framework for enhancing data security and privacy in advanced wireless communication systems. AES and RSA, renowned for their encryption capabilities, are integrated for secure data transmission and key exchange processes in 6G networks. Moreover, the incorporation of zero-knowledge proofs adds an additional layer of security, allowing entities to validate their knowledge without compromising sensitive information. Through extensive simulations and analyses, the effectiveness of the proposed algorithm to ensure secure communication within 6G networks is demonstrated. The algorithm is able to reduce security threats and vulnerabilities. This research lays the groundwork for the development of resilient and trustworthy next-generation communication infrastructures. Finally, the integration of AES, RSA, and zero-knowledge proofs presents a favorable approach to strengthen data security in 6G networks, paving the way for more reliable and secure wireless communication technologies in the future.
Future generations of wireless networks at high-frequency spectrum suffer from limited coverage and Non-Line- of-Sight signal blockage, challenging emerging applications, such as smart industries and intelligent automation systems. Collaborative and cooperative communications with smart relays via Non-Orthogonal Multiple Access (NOMA) could be a breakthrough solution to this challenge. This paper presents a blockchain-integrated framework for NOMA wireless communication systems that incentivizes cooperation among users serving as relays. By leveraging Ethereum-based smart contracts, we introduce a Service Verification Contract featuring a Proof of Quality of Experience (PQoE) mechanism. The contract uses trust scores, weighted verifications, and dynamic validation thresholds to ensure honest behavior and deter malicious activities. The simulation results show that honest participants gradually increase their trust scores and require fewer verifications, while malicious verifiers lose influence over repeated rounds. Our findings indicate that combining trust-based incentives with a decentralized ledger can effectively promote reliable data-relaying services and streamline payment processes in collaborative and smart wireless networking systems.
As a critical component in federated learning (FL), secure aggregation enables the server to learn the aggregated model without observing clients’ local training gradients. However, limited by computation and communication capabilities, existing aggregation schemes are not suitable to be directly employed in the Vehicular Ad Hoc Networks (VANETs) scenario. In this paper, we present a secure aggregation framework constructed with k-regular graph over VANETs scenario. We first optimize the secure aggregation scheme proposed by Bell et al. (CCS 2020). Specifically, using this new building block and an identity authentication mechanism in the vehicle-to-vehicle (V2V) communication mode, we design an optimized aggregation scheme that, when executed among n vehicles, can further reduce$2n$communication times between vehicles and the central server while guaranteeing logarithmic overhead. Besides, by applying a zero-knowledge proof to the authentication process, our proposal supports vehicles anonymously constructing the k-regular graph and completing parameter computation process, which enhances privacy preservation in semi-honest settings. Under the experiment and security analysis, our proposal is demonstrated to be able to effectively achieve privacy preservation while achieving less computation and communication overheads compared to state-of-the-art aggregation schemes.
Yushintia Pramitarini, Ridho Hendra Yoga Perdana, Kyusung Shim, Beongku An
In this paper, we propose a novel federated blockchain (FedChain)-based clustering protocol to enhance network security and connectivity in flying ad hoc networks (FANETs) with cell-free massive MIMO (CF-mMIMO). By leveraging blockchain technology and federated learning (FL), the cluster can be protected against Sybil attacks, enabling secure cluster formation without increasing the number of control packets. We formulate the cost function maximization problem based on cross-layer design, which integrates physical layer information (mobility, position, channel capacity, and remaining energy) and network layer parameters (connectivity) to optimize the formation of stable clusters with minimal control overhead. Furthermore, we select the optimal cluster heads (CHs) based on the highest remaining energy and velocity-constrained criteria, ensuring long-term stability. To solve the security issue, blockchain technology is adopted to validate transactions among nodes and ensure secure formation by distinguishing legitimate users and Sybil attack nodes. Additionally, we develop a novel FL framework to predict and distinguish node status in real time without additional control packets, improving security and control overhead performance during cluster formation. Simulation results demonstrate that the proposed FedChain-based clustering protocol outperforms the lowest ID (LI), high connectivity degree (HCD), and conventional blockchain-based clustering (CBC) protocols in terms of connectivity, control overhead, and security performance. The results highlight that the FedChain-based clustering protocol provides robust security and connectivity, making it well-suited for dynamic FANET environments.
Physical unclonable function (PUF) is a critical hardware primitive that provides unique identities for authenticating a large number of devices in the Industrial Internet of Things (IIoT). Most existing PUF-based schemes face challenge-response pair (CRP) leakage during machine-learning attack. Some studies that use hardware or time-consuming cryptographic operations to protect the PUF responses are expensive and unsuitable for existing IIoT devices. To address these issues, a lightweight and anonymous PUF-based authentication scheme is proposed for resource-constrained IIoTs. Using elliptic curve cryptography and zero-knowledge proof, a lightweight blinding mechanism is designed in the proposed scheme that prevents explicit CRP leakage and ensures anonymity. In addition, the authenticated keys are random with forward and backward secrecy. Moreover, the security of the proposed scheme is demonstrated using a random oracle model. Experimental results demonstrate that the proposed scheme is notably more efficient and practical for resource-constrained devices compared to other related schemes.
Open access
Physical Unclonable Functions (PUFs) and Hardware Security
Dadmehr Rahbari, Masoud Daneshtalab, Maksim Jenihhin
With the rapid growth of edge AI applications, there is an increasing demand for federated learning (FL) frameworks that are both efficient and privacy-preserving. This work introduces a robust approach that leverages homomorphic encryption (HE) to ensure data confidentiality during decentralized training. To tackle the typical challenges of FL—such as high communication overhead, resource limitations, and convergence inefficiencies—the method integrates dynamic client clustering, quantization-aware training, and structured model pruning. These optimizations collectively reduce latency and memory consumption while accelerating model convergence. Evaluations using the Human Activity Recognition dataset show that the proposed approach outperforms several state-of-the-art FL methods, achieving an average +8.4% improvement in accuracy, -16.2% lower latency, -35.1% reduction in memory usage, and -2.7% lower security overhead. These results demonstrate its suitability for real-time, resource-constrained scenarios in domains like healthcare, IoT, and finance, where maintaining a strong balance between efficiency and privacy is essential.
Recent revolutionary advancements in the services as observed with the use cases of Industry 5.0, consumer electronics 2.0/smart devices 2.0, digital healthcare ecosystem, Internet-of-Things (IoT), advanced digital finance/currency, and Non-Terrestrial Network (NTN) expansion, to name a few, have resulted in a spectacular growth in the number of wireless-connected devices. Subsequently, this has drastically increased the demands for network capacity, channel capacity, reliability, privacy, and security provisions. Despite that, the 5 th Generation (5G) of wireless communication networks has introduced various innovative services such as Ultra-Reliable Low Latency Communication (URLLCs), Massive Machine Type Communication (mMTCs), and Enhanced Mobile Broadband (eMBB). These services only support isolated operations and the requisite reliable service delivery remains a challenge. The Beyond 5G (B5G)/6 th Generation (6G) wireless networks aim at simultaneously providing multiple integrated services through intelligent network operations with ultra-high speed and reliability supporting integrated NTN and terrestrial networks. However, the prospect of such an extensively connected decentralized 3D wireless network also foresees security concerns, underscoring the necessity for seamless and infrastructurefree (decentralized) security solutions. The conventional security mechanisms are considered inadequate to ensure the security provisions of such extensive, decentralized, and heterogeneous networks. Physical Layer Security (PLS) is a promising technique to extend seamless and infrastructure-less security solutions, ensuring the availability, confidentiality, and integrity of legitimate transmissions. This paper provides a comprehensive overview with tutorials and presents the state-of-the-art of PLS, focusing mainly on NTN wireless communications. Furthermore, current research challenges, open issues, and future research directions are also thoroughly discussed in an amalgamation of various emerging 6G technologies. Finally, we provide an overview of implementation challenges in NTN and potential solutions to support the standardization progression of NTN in upcoming releases of 3 rd Generation Partnership Project (3GPP).
Medical risk management is a critical process within healthcare institutions that involves identifying, assessing, and mitigating risks to ensure patient safety and improve care quality. In recent years, the Internet of Medical Things (IoMT) has proved effective in monitoring patient health, particularly during disasters and epidemics such as COVID-19. However, the transmission of information over open channels makes such networks vulnerable to potential attacks. In addition, quantum computing presents a significant threat to current cryptographic algorithms. While existing solutions protect against well-known threats, few address the issue of quantum attacks. To address these challenges, this work presents an effective group authentication scheme for IoMT systems, leveraging post-quantum security using Kyber-PKE and Dilithium, Shamir’s secret sharing (SSS) algorithm, and smart contract. The proposed solution was simulated on the Ethereum platform and evaluated using Hyperledger Caliper, demonstrating its efficiency and scalability. A comparative analysis to recent pertinent works in terms of computation cost, power consumption, and security requirements shows that our system is well suited for various IoMT applications due to its robustness and efficiency.
The WiFi fingerprint-based localization method is considered one of the most popular techniques for indoor localization. In INFOCOM'14, Li et al. proposed a wireless fidelity (WiFi) fingerprint localization system based on Paillier encryption, which is claimed to protect both client$C{{}^{\prime}\mathrm{s}}$location privacy and service provider$S{{}^{\prime}\mathrm{s}}$database privacy. However, Yang et al. presented a practical data privacy attack in INFOCOM'18, which allows a polynomial time attacker to obtain$S{{}^{\prime}\mathrm{s}}$database. We propose a novel WiFi fingerprint localization system based on Castagnos-Laguillaumie (CL) encryption, which has a trustless setup and is efficient due to the excellent properties of CL encryption. To prevent Yang et al.'s attack, the system requires that$S$selects only the locations from its database that can receive the nonzero signals from all the available access points in$C{{}^{\prime}\mathrm{s}}$nonzero fingerprint in order to determine$C{{}^{\prime}\mathrm{s}}$location. Security analysis shows that our scheme is secure under Li et al.'s threat model. Furthermore, to enhance the security level of privacy-preserving WiFi fingerprint localization scheme based on CL encryption, we propose a secure and efficient zero-knowledge proof protocol for the discrete logarithm relations in$C{{}^{\prime}\mathrm{s}}$encrypted localization queries.
This work presents an innovative algorithm demonstrating the effectiveness of zero-knowledge proofs (ZKPs) in network security. By integrating Advanced Encryption Standard (AES) and Rivest-Shamir-Adleman (RSA) for key generation, the algorithm showcases their applicability in enhancing security measures within 6G networks. It highlights the utility of ZKPs in bolstering data privacy and security by enabling entities to validate knowledge without compromising sensitive information. The algorithm shows its capability to ensure robust communication security through comprehensive simulations, thereby laying the groundwork for dependable next-generation communication infrastructures.
In sectors such as finance and healthcare, where data governance is subject to rigorous regulatory requirements, the exchange and utilization of data are particularly challenging. Federated Learning (FL) has risen as a pioneering distributed machine learning paradigm that enables collaborative model training across multiple institutions while maintaining data decentralization. Despite its advantages, FL is vulnerable to adversarial threats, particularly poisoning attacks during model aggregation, a process typically managed by a central server. However, in these systems, neural network models still possess the capacity to inadvertently memorize and potentially expose individual training instances. This presents a significant privacy risk, as attackers could reconstruct private data by leveraging the information contained in the model itself. Existing solutions fall short of providing a viable, privacy-preserving BRFL system that is both completely secure against information leakage and computationally efficient. To address these concerns, we propose Lancelot, an innovative and computationally efficient BRFL framework that employs fully homomorphic encryption (FHE) to safeguard against malicious client activities while preserving data privacy. Our extensive testing, which includes medical imaging diagnostics and widely-used public image datasets, demonstrates that Lancelot significantly outperforms existing methods, offering more than a twenty-fold increase in processing speed, all while maintaining data privacy.
The Internet of Medical Things (IoMT) has emerged substantial growth within the healthcare sector, spurred by advancements in smart devices that generate and process critical healthcare data. Sharing this data among trusted healthcare entities is vital for efficient patient care but raises substantial security and privacy concerns. Existing solutions have focused on authentication techniques employing Self-Sovereign Identity (SSI) and Zero-Knowledge Proofs (ZKPs), aiming to ensure secure, auditable, and anonymous authentication. These solutions often prioritize either lightweight authentication or robust security, yet a flexible approach that integrates both characteristics is essential for adaptive cross-domain authentication in the IoMT landscape. Furthermore, the importance of lightweight authentication combined with adaptive verification, pivotal for scaling access control in cross-domain environments, has been underexplored in existing frameworks. Addressing this gap, we proposed a scheme called LSAC which is a lightweight, scalable, and anonymous authentication for SSI-based cross-domain authentication for IoMT setting. Our proposed LSAC scheme not only facilitates rapid and robust identity verification across multiple IoMT domains within a consortium blockchain network but also incorporates the advanced ZK-STARK and Plonk ZKP protocols to enable efficient and secure verification processes. By leveraging Hyperledger for generating smart contracts and managing user interactions across domains, our system represents a significant step forward in cross-domain authentication. Our comprehensive functionality and performance analysis confirms the effectiveness of our solution in providing scalable and secure access to IoMT resources, supporting the ongoing evolution of healthcare services.
Matheus V. X. Ferreira, Aadityan Ganesh, Jack Hourigan, Hannah Huh · 6 authors
Cryptographic Self-Selection is a paradigm employed by modern Proof-of-Stake consensus protocols to select a block-proposing "leader." Algorand [Chen and Micali, 2019] proposes a canonical protocol, and Ferreira et al. [2022] establish bounds $f(α,β)$ on the maximum fraction of rounds a strategic player can lead as a function of their stake $α$ and a network connectivity parameter $β$. While both their lower and upper bounds are non-trivial, there is a substantial gap between them (for example, they establish $f(10\%,1) \in [10.08\%, 21.12\%]$), leaving open the question of how significant of a concern these manipulations are. We develop computational methods to provably nail $f(α,β)$ for any desired $(α,β)$ up to arbitrary precision, and implement our method on a wide range of parameters (for example, we confirm $f(10\%,1) \in [10.08\%, 10.15\%]$). Methodologically, estimating $f(α,β)$ can be phrased as estimating to high precision the value of a Markov Decision Process whose states are countably-long lists of real numbers. Our methodological contributions involve (a) reformulating the question instead as computing to high precision the expected value of a distribution that is a fixed-point of a non-linear sampling operator, and (b) provably bounding the error induced by various truncations and sampling estimations of this distribution (which appears intractable to solve in closed form). One technical challenge, for example, is that natural sampling-based estimates of the mean of our target distribution are \emph{not} unbiased estimators, and therefore our methods necessarily go beyond claiming sufficiently-many samples to be close to the mean.
Muhammad Rizwan, Mudassir Ali, Ammar Hawbani, Xingfu Wang · 8 authors
Vehicle-to-grid (V2G) energy trading based on distributed ledger technologies (DLT), such as blockchains, has attracted much attention due to its promising features, including ease of deployment, decentralization, transparency, and security. However, existing DLT-based models do not support microtransactions due to the low value of such transactions relative to the incentives offered to transaction verifiers. To address this issue, we propose an IOTA DLT-based efficient and secure energy trading model for V2G networks, where electric vehicles (EVs) and grids negotiate energy prices in an off-chain manner. The proposed model utilizes a privacy-preserving protocol to prevent real-time tracking of EV locations. We develop a Stackelberg game model to represent the interactions between the EVs and grids, from which we derive a pricing scheme and propose a deposit mechanism to prevent fake energy trading between the EVs and grids. Extensive simulations demonstrate that our proposed scheme outperforms existing V2G energy trading mechanisms regarding transaction efficiency, provides enhanced EV privacy, and improves resilience against fake energy trading. Offering robust computational performance and addressing computational complexity (time, space, and message), our model presents a comprehensive V2G energy trading solution, balancing efficiency, security, and privacy.
Zakaria Abou El Houda, Hajar Moudoud, Lyes Khoukhi
O-RAN (Open Radio Access Network) is an initiative that promotes the development of open and interoperable radio access technologies. The O-RAN Alliance has undertaken specification efforts that align with O-RAN principles, incorporating the near-real-time RAN Intelligent Controller (RIC) to manage extensible applications (xApps) owned by various ORAN operators and vendors. However, this integration of untrusted third-party applications raises significant security concerns, expanding the threat surface of 6G networks. Moreover, the heterogeneity in deployment, with apps residing on various sites, poses challenges for traditional security models based on perimeter security. To overcome this issue, a Zero Trust Architecture (ZTA) becomes paramount to ensure network security. In this context, we introduce TrustORAN, a novel blockchain-based decentralized Zero-Trust Framework designed to ensure security and trustworthiness in O-RAN. TrustORAN allows for the verification and authentication of xApps by O-RAN players, to prevent unauthorized access from malicious xApps. Moreover, we introduce a dynamic decentralized-based access control framework that allows vendors to manage permissions in a fully decentralized, flexible, scalable, and secure manner. TrustORAN architecture is implemented, tested, and deployed on both private and public blockchains. The obtained results demonstrate that TrustORAN empowers 6G O-RAN networks with heightened security, resilience, and robustness, providing effective protection against evolving security threats while ensuring Trust.
Bo Zhang, Tao Zhang, Zesheng Xi, Ping Chen · 6 authors
With the rapid development of the Internet of Things (IoT), ensuring secure communication between devices has become a crucial challenge. This paper proposes a novel secure communication solution by extracting wireless channel state information (CSI) features from IoT devices to generate a device identity. Due to the instability of the wireless channel, the CSI features are fuzzy and time-varying; thus, we a employ locally sensitive hashing (LSH) algorithm to ensure the stability of the generated identity in a dynamically changing wireless channel environment. Furthermore, zero-knowledge proofs are utilized to guarantee the authenticity and effectiveness of the generated identity. Finally, the identity generated using the aforementioned approach is integrated into an IBE communication scheme, which involves the fuzzy extraction of channel state information from IoT devices, stable identity extraction for fuzzy IoT devices using LSH, and the use of zero-knowledge proofs to ensure the authenticity of the generated identity. This identity is then employed as the identity information in identity-based encryption (IBE), constructing the device’s public key for achieving confidential communication between devices.
Krishna Murthy Kattiyan Ramamoorthy, Wei Wang, K. Sohraby, Yanxiao Zhao
In Non-Orthogonal Multiple Access (NOMA) wireless networks, it can be beneficial to allow closer users to relay the cache data to farther users. However, motivating short-distance NOMA users to participate in the relaying requires an appropriate incentive. In this paper, we propose a new crypto token - NOMAToken on the Ethereum blockchain leveraging the Proof of Quality of Experience (QoE) consensus mechanism. NOMAToken serves as a payment token that facilitates all monetary transactions within a NOMA network. As an Ethereum-based token, it can be held or traded against reserve tokens, establishing its own price. The optimal price for retransmission services is determined using the Vickery-Clarke-Groves (VCG) second price auction technique. We discuss the Proof-of-QoE driven consensus model and a Prospect Theory inspired scoring model to regulate the token. The consensus model is designed to ensure that the relay provides the highest possible QoE for its users, while the scoring mechanism serves as a paradigm to allow users to mint new NOMAToken and introduce liquidity.
Minjae Seo, Jaehan Kim, Myoungsung You, Seungwon Shin · 5 authors
Blockchain technology has ushered in a transformative paradigm of decentralized and transparent systems, offering innovative solutions across diverse sectors. While these systems strive for unparalleled transparency and trustlessness in a fully distributed framework, permissionless blockchains, such as Bitcoin and Ethereum, encounter vulnerabilities due to their intrinsically public nature. Addressing these vulnerabilities, the emergence of permissioned blockchains presents a fortified alternative, incorporating rigorous access controls and authentication protocols to ensure participation exclusivity and transaction confidentiality. Nevertheless, a keen observation reveals that, despite encryption, the operational traffic within these blockchains manifests distinct time-series patterns and operational relations during sensitive data exchanges. Such patterns hold the potential to inadvertently expose critical details about the network, encompassing its topology and the operational dependencies among nodes. In light of this revelation, we introduce a pioneering blockchain fingerprinting mechanism, denoted as gShock. This system meticulously analyzes periodic patterns and the context of operational relations from the collected blockchain network traffic. It employs a Graph Neural Network (GNN)-based model, adept at capturing the intricate characteristics innate to specialized blockchain operations. Through empirical experiments conducted in a realistic permissioned blockchain environment, comprising various nodes, we ascertain that gShock demonstrates a remarkable proficiency in classifying blockchain operational traffic with an F1-score of$\geq 96$% and identifying individual dependencies with a macro F1-score of$\geq 93$%.
Open access
Advanced Steganography and Watermarking Techniques
David Cordova Morales, Thi Mai Trang Nguyen, Guy Pujolle
One of the most important paradigm shifts nowa-days regarding future 6G communication is related, from one side to the desire of bringing services and data as close as possible to the end users, and from another side, to empower the user with control over their data and personal information. In this vision, private 6G networks will shape trusted zones where data center services are placed at the edge of the network. The services will follow a Web3 approach, where decentralization and zero-trust mechanisms are predominant. In this environment, a decen-tralized authentication mechanism is needed. In this paper, we propose a 6G architecture and a blockchain-like authentication scheme based on Verifiable Credentials. Our model uses zero-trust technology for a better and more trusted Internet.
Wenxuan Ye, Chendi Qian, Xueli An, Xueqiang Yan · 5 authors
Integrating native AI support into the network architecture is an essential objective of 6G. Federated Learning (FL) emerges as a potential paradigm, facilitating decentralized AI model training across a diverse range of devices under the co-ordination of a central server. However, several challenges hinder its wide application in the 6G context, such as malicious attacks and privacy snooping on local model updates, and centralization pitfalls. This work proposes a trusted architecture for supporting FL, which utilizes Distributed Ledger Technology (DLT) and Graph Neural Network (GNN), including three key features. First, a pre-processing layer employing homomorphic encryption is incorporated to securely aggregate local models, preserving the privacy of individual models. Second, given the distributed nature and graph structure between clients and nodes in the pre-processing layer, GNN is leveraged to identify abnormal local models, enhancing system security. Third, DLT is utilized to decentralize the system by selecting one of the candidates to perform the central server's functions. Additionally, DLT ensures reliable data management by recording data exchanges in an immutable and transparent ledger. The feasibility of the novel architecture is validated through simulations, demonstrating improved performance in anomalous model detection and global model accuracy compared to relevant baselines.
Roberto Aparici Marino, Lorenzo Carnevale, Massimo Villari
Federated Learning (FL) is a cutting-edge technology for distributed solving of large-scale problems using local data exclusively. The potential of Federated Learning is nowadays clear in different context from automatic analysis of healthcare data to object recognition in video sources coming from public video streams, from distributed search for data breach and finance frauds to collaborative learning of hand typing on mobile phone. Multi-robot systems can also largely benefit from FL concerning resolution of problems like trajectory prediction, non colliding trajectory generation, distributed localization and mapping or distributed reinforcement learning. In this paper we propose a multi-robot framework that includes distributed learning capabilities by using Decentralized Stochastic Gradient Descent on graphs. First of all we motivate the position of the paper discussing the privacy preserving problem for multi robot systems and the need of decentralized learning. Then we build our methodology starting from a set of prior definitions. Finally we discuss in details the possible applications in robotics field.