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.
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.
Esraa M. Ghourab, Lina Bariah, Sami Muhaidat, Paschalis C. Sofotasios · 6 authors
The highly dynamic nature of cognitive radio systems (CR) and their stringent latency requirements pose a major challenge in the realization of efficient intelligent transportation systems. In this paper, we investigate relay selection and opportunistic spectrum access in conjunction with blockchain technology. In particular, we propose a cross-layer method for secure relay selection, where secondary relays (SRs) are granted access to available spectrum bands based on the balance of their respective virtual wallets. These virtual wallets, which are built based on the SRs' secrecy capacity and their behavior in the network, are the predominant factors that allow SRs to participate in an auction model. To quantify the trustworthiness of SRs, we formulate a mathematical framework to evaluate the trust value of each SR, which is then leveraged for rewarding or penalizing the SR. We develop an offline module blockchain framework to store the real-time information of participating relays and make it available for future operations. The information is checked and verified by miners. Our system is able to detect reputable and non-reputable relays in the presence of multiple eavesdroppers. We present a thorough numerical analysis to demonstrate the superiority of the proposed scheme in terms of security, credibility, and integrity. On average, the secrecy capacity rate of the overall system increased by 70% compared to the traditional systems. Further, the propsed model perform better in low SNR compared to traditional one.
Adnan Shahid Khan, Mohd Izzat Bin Yahya, Kartinah Zen, Johari Abdullah · 8 authors
Cell-Free mMIMO is a part of technology that will be integrated with future 6G ultra-dense cellular networks to ensure unlimited wireless connectivity and ubiquitous latency-sensitive services. Cell-Free gained researchers’ interest as it offers ubiquitous communication with large bandwidth, high throughput, high data transmission, and greater signal gain. Cell-Free eliminates the idea of cell boundary in cellular communication that reduces frequent handover and inter-cell interference issues. However, the effectiveness of the current authentication protocol could become a serious issue due to the dynamic nature of Cell-Free in densely distributed, high number of users, high mobility, and frequent data exchange. Secondly, secure communication may be achieved in such a dynamic environment at the expense of high authentication overhead, high communication and computational costs. To address the above security challenges, we proposed a lightweight multifactor mutual authentication protocol for Cell-Free communication using ECC-based Deffie Hellman (ECDH). This scheme utilizes timestamping, one-way hash function, Blind-Fold Challenge scheme with public key infrastructure. The proposed cryptosystem integrates with blockchain technology using proof of staked (POS) as a consensus mechanism to ensure integrity, non-repudiation and traceability. The proposed scheme can enforce the mitigation of several major security attacks on communication links such as spoofing attacks, eavesdropping, user location privacy issues, replay attacks, denial of service attacks, and man-in-the-middle (MITM) attacks, which is one of the significant features of the scheme. Furthermore, this scheme contributes to reducing authentication, communication, and computational overheads with an average of 32.8%, 52.4% and 53.2% better performance respectively as compared baseline authentication protocols.
The recent technological advancements in wireless communication systems and the Internet of Things (IoT) have accelerated the development of zero touch networks (ZTNs). ZTNs provide self-monitoring, self-configuring, and automated service-level policies that cannot be fulfilled by the traditional network management and orchestration approaches. Despite the hype, the majority of data exchange between participating entities occurs over insecure public channels, which present a number of possible security risks and attacks. Toward this end, we first analyze the attack surface on IoT-enabled ZTNs and the inherent architectural flaws for such threats. After an overview of attack surface, this article presents a new deep-learning- and blockchain-assisted case study for secure data sharing in ZTNs. Specifically, first, we design a novel variational autoencoder (VAE) and attention-based gated recurrent units (AGRU)-based intrusion detection system (IDS) for ZTNs. Second, a novel authentication protocol that combines blockchain, smart contracts (SCs), elliptic curve cryptography (ECC), and a proof of authority (PoA) consensus mechanism is developed to improve secure data sharing in ZTNs. The extensive experimental results show the effectiveness of the proposed approach. Lastly, this work discusses critical issues, opportunities, and open research directions to solve these challenges.
Recently, unmanned aerial vehicles (UAVs) are deployed in Novel Coronavirus Disease-2019 (COVID-19) vaccine distribution process. To address issues of fake vaccine distribution, real-time massive UAV monitoring and control at nodal centers (NCs), the authors propose SanJeeVni, a blockchain (BC)-assisted UAV vaccine distribution at the backdrop of sixth-generation (6G) enhanced ultra-reliable low latency communication (6G-eRLLC) communication. The scheme considers user registration, vaccine request, and distribution through a public Solana BC setup, which assures a scalable transaction rate. Based on vaccine requests at production setups, UAV swarms are triggered with vaccine delivery to NCs. An intelligent edge offloading scheme is proposed to support UAV coordinates and routing path setups. The scheme is compared against fifth-generation (5G) uRLLC communication. In the simulation, we achieve and 86% improvement in service latency, 12.2% energy reduction of UAV with 76.25% more UAV coverage in 6G-eRLLC, and a significant improvement of [Formula: see text]% in storage cost against the Ethereum network, which indicates the scheme efficacy in practical setups.
Manal M. Khayyat, Mashael Khayyat, S. Abdel‐Khalek, Romany F. Mansour
Industrial Internet of Things (IIoT) denotes a network of interlinked sensors, instruments, and other devices for industrial applications in the domains of manufacturing, logistics, transportation, etc. IIoT security is a major crucial research area for several applications. Image encryption techniques gained popularity in the recent years, thanks to increasing requirements for secure image transmission in IIoT environments. At the same time, conventional security solutions built for sensitive data protection are getting outdated in IIoT environment due to the participation of third party. Blockchain (BC) is one of the recent solutions used for security purpose which eliminates the involvement of a third party. With this motivation, the current research article presents a new BC-Enabled Shark Smell Optimization with Hopfield Chaotic Neural Network (SSO-HCNN) for secure encryption in IoT environment. The proposed SSO-HCNN model exploits a composite Chaotic Map (CM) which is integrated into staged logistic and tent maps to initially process the images and develop the variables needed for Arnold mapping. In addition, the SSO algorithm is developed with maximum PSNR and coefficient fitness function to select the optimum secret and public keys of the system amongst the random numbers. Besides, the diffusion phase utilizes HCNN to create a self-diffusion chaotic matrix whereas the jumbled image performs XOR operation using the keys to obtain the cipher image. In SSO-HCNN model, the cryptographic pixel value in the image is saved on BC thus guaranteeing the security and privacy of the images. To examine the superior performance of SSO-HCNN model over state-of-the-art methods, a set of simulations was conducted on benchmark test images. The simulation results of the proposed SSO-HCNN model were promising under different evaluation parameters.
Open access
Chaos-based Image/Signal Encryption
Advanced Steganography and Watermarking Techniques
This article investigates computation offloading in blockchain-empowered Internet of Things (IoT), where the task data uploading link from sensors to a base station (BS) is protected by intelligent reflecting surface (IRS)-assisted physical-layer security (PLS). After receiving task data, the BS allocates computational resources provided by mobile-edge computing (MEC) servers to help sensors perform tasks. Existing blockchain-based computation offloading schemes usually focus on network performance improvements, such as energy consumption minimization (ECM) or latency minimization, and neglect the Gas fee for computation offloading, resulting in the dissatisfaction of high Gas providers. Also, the secrecy rate during the data uploading process cannot be measured by a steady value because of the time-varying characteristics of IRS-based wireless channels, thereby computational resources allocation with a secrecy rate measured before data uploading is inappropriate. In this article, we design a Gas-oriented computation offloading scheme that guarantees a low degree of dissatisfaction of sensors, while reducing energy consumption. Also, we deduce the ergodic secrecy rate of IRS-assisted PLS transmission that can represent the global secrecy performance to allocate computational resources. The simulations show that the proposed scheme has lower energy consumption compared to existing schemes and ensures that the node paying higher Gas gets stronger computational resources.
Shimaa Abdelnaby AbdelHakeem, Hanan Hussein, HyungWon Kim
After implementing 5G technology, academia and industry started researching 6th generation wireless network technology (6G). 6G is expected to be implemented around the year 2030. It will offer a significant experience for everyone by enabling hyper-connectivity between people and everything. In addition, it is expected to extend mobile communication possibilities where earlier generations could not have developed. Several potential technologies are predicted to serve as the foundation of 6G networks. These include upcoming and current technologies such as post-quantum cryptography, artificial intelligence (AI), machine learning (ML), enhanced edge computing, molecular communication, THz, visible light communication (VLC), and distributed ledger (DL) technologies such as blockchain. From a security and privacy perspective, these developments need a reconsideration of prior security traditional methods. New novel authentication, encryption, access control, communication, and malicious activity detection must satisfy the higher significant requirements of future networks. In addition, new security approaches are necessary to ensure trustworthiness and privacy. This paper provides insights into the critical problems and difficulties related to the security, privacy, and trust issues of 6G networks. Moreover, the standard technologies and security challenges per each technology are clarified. This paper introduces the 6G security architecture and improvements over the 5G architecture. We also introduce the security issues and challenges of the 6G physical layer. In addition, the AI/ML layers and the proposed security solution in each layer are studied. The paper summarizes the security evolution in legacy mobile networks and concludes with their security problems and the most essential 6G application services and their security requirements. Finally, this paper provides a complete discussion of 6G networks' trustworthiness and solutions.
Although the fifth generation wireless networks are yet to be fully investigated, the vision and key elements of the 6th generation (6G) ecosystem have already come into discussion. In order to contribute to these efforts and delineate the security and privacy aspects of 6G networks, we survey how security may impact the envisioned 6G wireless systems with the possible challenges and potential solutions. Especially, we discuss the security and privacy challenges that may emerge with the 6G requirements, novel network architecture, applications and enabling technologies including distributed ledger technologies, physical layer security, distributed artificial intelligence (AI)/ machine learning (ML), Visible Light Communication (VLC), THz bands, and quantum communication
We review probabilistic models known as majority dynamics (also known as threshold Voter Models) and discuss their possible applications for achieving consensus in cryptocurrency systems. In particular, we show that using this approach straightforwardly for practical consensus in Byzantine setting can be problematic and requires extensive further research. We then discuss the FPC consensus protocol which circumvents the problems mentioned above by using external randomness.
In the mid 2030-s in the field of defense and national security communications the quantum computers and 6G artificial intelligence will have domination. 6G communication is accepted in a variety of mobile data comparts transmitted through spectral technologies. The human body becomes a part of the 6G network architecture. A set of network nodes or wearable devices, embedded sensors or nanodes collect confidential information that is exchanged for multiple purposes, such as health, statistics, and safety. An important part of the 6G new paradigm will be intelligent reflective surfaces, quantum teleportation, quantum encrypted messaging, 6G holography, distributed ledger, 6G layer security threats. The 6G wireless standards will allow real-time time zone high-speed internet communication with 1TB data per second. The radio frequency networks, THZ communications, molecular communications, and quantum communications will dramatically improve data rates.
Although the fifth generation (5G) wireless networks are yet to be fully investigated, the visionaries of the 6th generation (6G) echo systems have already come into the discussion. Therefore, in order to consolidate and solidify the security and privacy in 6G networks, we survey how security may impact the envisioned 6G wireless systems, possible challenges with different 6G technologies, and the potential solutions. We provide our vision on 6G security and security key performance indicators (KPIs) with the tentative threat landscape based on the foreseen 6G network architecture. Moreover, we discuss the security and privacy challenges that may encounter with the available 6G requirements and potential 6G applications. We also give the reader some insights into the standardization efforts and research-level projects relevant to 6G security. In particular, we discuss the security considerations with 6G enabling technologies such as distributed ledger technology (DLT), physical layer security, distributed AI/ML, visible light communication (VLC), THz, and quantum computing. All in all, this work intends to provide enlightening guidance for the subsequent research of 6G security and privacy at this initial phase of vision towards reality.
Sixth-generation (6G) mobile networks will have to cope with diverse threats on a space-air-ground integrated network environment, novel technologies, and an accessible user information explosion. However, for now, security and privacy issues for 6G remain largely in concept. This survey provides a systematic overview of security and privacy issues based on prospective technologies for 6G in the physical, connection, and service layers, as well as through lessons learned from the failures of existing security architectures and state-of-the-art defenses. Two key lessons learned are as follows. First, other than inheriting vulnerabilities from the previous generations, 6G has new threat vectors from new radio technologies, such as the exposed location of radio stripes in ultra-massive MIMO systems at Terahertz bands and attacks against pervasive intelligence. Second, physical layer protection, deep network slicing, quantum-safe communications, artificial intelligence (AI) security, platform-agnostic security, real-time adaptive security, and novel data protection mechanisms such as distributed ledgers and differential privacy are the top promising techniques to mitigate the attack magnitude and personal data breaches substantially.
Latif U. Khan, Ibrar Yaqoob, Muhammad Imran, Zhu Han · 5 authors
Internet of everything (IoE)-based smart services are expected to gain immense popularity in the future, which raises the need for next-generation wireless networks. Although fifth-generation (5G) networks can support various IoE services, they might not be able to completely fulfill the requirements of novel applications. Sixth-generation (6G) wireless systems are envisioned to overcome 5G network limitations. In this article, we explore recent advances made toward enabling 6G systems. We devise a taxonomy based on key enabling technologies, use cases, emerging machine learning schemes, communication technologies, networking technologies, and computing technologies. Furthermore, we identify and discuss open research challenges, such as artificial-intelligence-based adaptive transceivers, intelligent wireless energy harvesting, decentralized and secure business models, intelligent cell-less architecture, and distributed security models. We propose practical guidelines including deep Q-learning and federated learning-based transceivers, blockchain-based secure business models, homomorphic encryption, and distributed-ledger-based authentication schemes to cope with these challenges. Finally, we outline and recommend several future directions.
Network coding has emerged as a promising solution to the highly efficient and reliable network requirements for the next generation communication technology. Network coding enabled small cell environment can assure an efficient device to device communication with the high data rate. However, the security challenges inherent to network coding, like pollution attacks, drags a layer of concern over its implementation. Further, the re-encoding of packets at the intermediate nodes makes it difficult to secure network coding enabled communication using standard cryptographic techniques. Homomorphic message authentication codes and signatures are used to tackle this problem. We propose a network coding enabled small cell environment enhanced by blockchain to prevent pollution attacks. The architecture of this blockchain enhanced SECRET small cell along with an analysis of the communication overhead and latency issues are discussed in this paper.
Amjad Saeed Khan, Yogachandran Rahulamathavan, Bokamoso Basutli, Gan Zheng · 6 authors
Physical layer security (PLS) is considered as a promising technique to prevent information eavesdropping in wireless systems. In this context, cooperative relaying has emerged as a robust solution for achieving PLS due to multipath diversity and relatively lower transmission power. However, relays or the relay operators in the practical environment are unwilling for service provisioning unless they are incentivized for their cost of services. Thus, it is required to jointly consider network economics and relay cooperation to improve system efficiency. In this paper, we consider the problem of joint network economics and PLS using cooperative relaying and jamming. Based on the double auction theory, we model the interaction between transmitters seeking for a particular level of secure transmission of information and relay operators for suitable relay and jammer assignment, in a multiple source-destination networks. In addition, theoretical analyses are presented to justify that the proposed auction mechanism satisfies the desirable economic properties of individual rationality, budget balance, and truthfulness. As the participants in the traditional centralized auction framework may take selfish actions or collude with each other, we propose a decentralized and trustless auction framework based on blockchain technology. In particular, we exploit the smart contract feature of blockchain to construct a completely autonomous framework, where all the participants are financially enforced by smart contract terms. The security properties of the proposed framework are also discussed.
Anik Islam, Mohammed Belal Uddin, Md. Fazlul Kader, Soo Young Shin
Non-orthogonal multiple access (NOMA) with successive interference cancellation receiver is considered as one of the most potent multiple access techniques to be adopted in future wireless communication networks. Data security in the NOMA transmission scheme is on much attention drawing issue. Blockchain is a distributed peer-to-peer network enables a way of protecting information from unauthorized access, tempering etc. By utilizing encryption techniques of blockchain, a secured data communication scheme using blockchain in NOMA is proposed in this paper. A two-phase encryption technique with key generation using different parameter is proposed. In the first-phase data is encrypted by imposing users' public key and in the second phase, a private key of the base station (BS) is engaged for encryption. Finally, the superiority of the proposed scheme over existing scheme is proven through a comparative study based on the different features.
A device-to-device (D2D) underlying cellular network is pervasive to support various wireless applications. However, due to the dramatic increase of data transmission in the network with limited amount of wireless resource, a few users may be required to temporarily disconnect from the network to avoid the interruption of data transmission in the whole network. A critical issue of determining the user access in D2D underlying networks is the authenticity of channel state information (CSI), and usually, a user with a higher CSI can be allocated a larger amount of wireless resource or have a higher probability of staying in the network. In this paper, we propose a blockchain consensus-based scheme to verify the authenticity of CSI and add the users who intentionally advocate a higher value of CSI into a fraud chain. Also, we consider both the cross-tier interference caused by a mobile user and the presence of a user in the fraud chain to determine the access of a user. The analysis results show that our proposed user access scheme can enhance the network performance by efficiently controlling the use access in mobile applications.
J. S. Hammudoglu, J. Sparreboom, J. I. Rauhamaa, J. K. Faber · 8 authors
We devised a mobile biometric-based authentication system only relying on local processing. Our Android open source solution explores the capability of current smartphones to acquire, process and match fingerprints using only its built-in hardware. Our architecture is specifically designed to run completely locally and autonomously, not requiring any cloud service, server, or permissioned access to fingerprint reader hardware. It involves three main stages, starting with the fingerprint acquisition using the smartphone camera, followed by a processing pipeline to obtain minutiae features and a final step for matching against other locally stored fingerprints, based on Oriented FAST and Rotated BRIEF (ORB) descriptors. We obtained a mean matching accuracy of 55%, with the highest value of 67% for thumb fingers. Our ability to capture and process a finger fingerprint in mere seconds using a smartphone makes this work usable in a wide range of scenarios, for instance, offline remote regions. This work is specifically designed to be a key building block for a self-sovereign identity solution and integrate with our permissionless blockchain for identity and key attestation.