Security for Multi-hop Communication of Two-tier Wireless Networks with Different Trust Degrees

Many effective strategies for enhancing network performance have been put forth for wireless communications' physical-layer security. Up until now, wireless communications security and privacy have been optimized based on a set assumption on the reliability or network tiers of certain wireless nodes. Eavesdroppers, unreliable relays, and trustworthy cooperative nodes are just a few examples of the various sorts of nodes that are frequently categorized. When working or sharing information for one another, wireless nodes in various networks may not always have perfect trust in one another. Modern wireless networks' security and privacy may be enhanced in large part by optimizing the network based on trust levels. To determine the path with the shortest total transmission time between the source and the destination while still ensuring that the private messages are not routed through the untrusted network tier, we put forth a novel approach. To examine the effects of the transmit SNR, node density, and the percentage of the illegitimate nodes on various network performance components, simulation results are provided

A Survey on Machine Learning-based Misbehavior Detection Systems for 5G and Beyond Vehicular Networks

Advances in Vehicle-to-Everything (V2X) technology and onboard sensors have significantly accelerated deploying Connected and Automated Vehicles (CAVs). Integrating V2X with 5G has enabled Ultra-Reliable Low Latency Communications (URLLC) to CAVs. However, while communication performance has been enhanced, security and privacy issues have increased. Attacks have become more aggressive, and attackers have become more strategic. Public Key Infrastructure (PKI) proposed by standardization bodies cannot solely defend against these attacks. Thus, in complementary of that, sophisticated systems should be designed to detect such attacks and attackers. Machine Learning (ML) has recently emerged as a key enabler to secure future roads. Various V2X Misbehavior Detection Systems (MDSs) have adopted this paradigm. However, analyzing these systems is a research gap, and developing effective ML-based MDSs is still an open issue. To this end, this paper comprehensively surveys and classifies ML-based MDSs as well as discusses and analyses them from security and ML perspectives. It also provides some learned lessons and recommendations for guiding the development, validation, and deployment of ML-based MDSs. Finally, this paper highlighted open research and standardization issues with some future directions

Physical Layer Security in Wireless Networks: Design and Enhancement.

PhDSecurity and privacy have become increasingly significant concerns in wireless communication networks, due to the open nature of the wireless medium which makes the wireless transmission vulnerable to eavesdropping and inimical attacking. The emergence and development of decentralized and ad-hoc wireless networks pose great challenges to the implementation of higher-layer key distribution and management in practice. Against this background, physical layer security has emerged as an attractive approach for performing secure transmission in a low complexity manner. This thesis concentrates on physical layer security design and enhancement in wireless networks. First, this thesis presents a new unifying framework to analyze the average secrecy capacity and secrecy outage probability. Besides the exact average secrecy capacity and secrecy outage probability, a new approach for analyzing the asymptotic behavior is proposed to compute key performance parameters such as high signal-to-noise ratio slope, power offset, secrecy diversity order, and secrecy array gain. Typical fading environments such as two-wave with diffuse power and Nakagami-m are taken into account. Second, an analytical framework of using antenna selection schemes to achieve secrecy is provided. In particular, transmit antenna selection and generalized selection combining are considered including its special cases of selection combining and maximal-ratio combining. Third, the fundamental questions surrounding the joint impact of power constraints on the cognitive wiretap channel are addressed. Important design insights are revealed regarding the interplay between two power constraints, namely the maximum transmit at the secondary network and the peak interference power at the primary network. Fourth, secure single carrier transmission is considered in the two-hop decode-andi forward relay networks. A two-stage relay and destination selection is proposed to minimize the eavesdropping and maximize the signal power of the link between the relay and the destination. In two-hop amplify-and-forward untrusted relay networks, secrecy may not be guaranteed even in the absence of external eavesdroppers. As such, cooperative jamming with optimal power allocation is proposed to achieve non-zero secrecy rate. Fifth and last, physical layer security in large-scale wireless sensor networks is introduced. A stochastic geometry approach is adopted to model the positions of sensors, access points, sinks, and eavesdroppers. Two scenarios are considered: i) the active sensors transmit their sensing data to the access points, and ii) the active access points forward the data to the sinks. Important insights are concluded

Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and Insights

Artificial Intelligence (AI) is expected to play an instrumental role in the next generation of wireless systems, such as sixth-generation (6G) mobile network. However, massive data, energy consumption, training complexity, and sensitive data protection in wireless systems are all crucial challenges that must be addressed for training AI models and gathering intelligence and knowledge from distributed devices. Federated Learning (FL) is a recent framework that has emerged as a promising approach for multiple learning agents to build an accurate and robust machine learning models without sharing raw data. By allowing mobile handsets and devices to collaboratively learn a global model without explicit sharing of training data, FL exhibits high privacy and efficient spectrum utilization. While there are a lot of survey papers exploring FL paradigms and usability in 6G privacy, none of them has clearly addressed how FL can be used to improve the protocol stack and wireless operations. The main goal of this survey is to provide a comprehensive overview on FL usability to enhance mobile services and enable smart ecosystems to support novel use-cases. This paper examines the added-value of implementing FL throughout all levels of the protocol stack. Furthermore, it presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments. Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry and sustain the development of cutting-edge mobile services.Comment: 32 pages, 7 figures; 9 Table

MM-Wave HetNet in 5G and beyond Cellular Networks Reinforcement Learning Method to improve QoS and Exploiting Path Loss Model

This paper presents High density heterogeneous networks (HetNet) which are the most promising technology for the fifth generation (5G) cellular network. Since 5G will be available for a long time, previous generation networking systems will need customization and updates. We examine the merits and drawbacks of legacy and Q-Learning (QL)-based adaptive resource allocation systems. Furthermore, various comparisons between methods and schemes are made for the purpose of evaluating the solutions for future generation. Microwave macro cells are used to enable extra high capacity such as Long-Term Evolution (LTE), eNodeB (eNB), and Multimedia Communications Wireless technology (MC), in which they are most likely to be deployed. This paper also presents four scenarios for 5G mm-Wave implementation, including proposed system architectures. The WL algorithm allocates optimal power to the small cell base station (SBS) to satisfy the minimum necessary capacity of macro cell user equipment (MUEs) and small cell user equipment (SCUEs) in order to provide quality of service (QoS) (SUEs). The challenges with dense HetNet and the massive backhaul traffic they generate are discussed in this study. Finally, a core HetNet design based on clusters is aimed at reducing backhaul traffic. According to our findings, MM-wave HetNet and MEC can be useful in a wide range of applications, including ultra-high data rate and low latency communications in 5G and beyond. We also used the channel model simulator to examine the directional power delay profile with received signal power, path loss, and path loss exponent (PLE) for both LOS and NLOS using uniform linear array (ULA) 2X2 and 64x16 antenna configurations at 38 GHz and 73 GHz mmWave bands for both LOS and NLOS (NYUSIM). The simulation results show the performance of several path loss models in the mmWave and sub-6 GHz bands. The path loss in the close-in (CI) model at mmWave bands is higher than that of open space and two ray path loss models because it considers all shadowing and reflection effects between transmitter and receiver. We also compared the suggested method to existing models like Amiri, Su, Alsobhi, Iqbal, and greedy (non adaptive), and found that it not only enhanced MUE and SUE minimum capacities and reduced BT complexity, but it also established a new minimum QoS threshold. We also talked about 6G researches in the future. When compared to utilizing the dual slope route loss model alone in a hybrid heterogeneous network, our simulation findings show that decoupling is more visible when employing the dual slope path loss model, which enhances system performance in terms of coverage and data rate

Expansive networks : exploiting spectrum sharing for capacity boost and 6G vision

Adaptive capacity with cost-efficient resource provisioning is a crucial capability for future 6G networks. In this work, we conceptualize "expansive networks" which refers to a networking paradigm where networks should be able to extend their resource base by opportunistic but self-controlled expansive actions. To this end, we elaborate on a key aspect of an expansive network as a concrete example: Spectrum resource at the PHY layer. Evidently, future wireless networks need to provide efficient mechanisms to coexist in the licensed and unlicensed bands and operate in expansive mode. In this work, we first describe spectrum sharing issues and possibilities in 6G networks for expansive networks. We then present security implications of expansive networks, an important concern due to more open and coupled systems in expansive networks. We also discuss two key enablers, namely distributed ledger technology (DLT) and network intelligence via machine learning, which are promising to realize expansive networks for the spectrum sharing aspect

Self-Evolving Integrated Vertical Heterogeneous Networks

6G and beyond networks tend towards fully intelligent and adaptive design in order to provide better operational agility in maintaining universal wireless access and supporting a wide range of services and use cases while dealing with network complexity efficiently. Such enhanced network agility will require developing a self-evolving capability in designing both the network architecture and resource management to intelligently utilize resources, reduce operational costs, and achieve the coveted quality of service (QoS). To enable this capability, the necessity of considering an integrated vertical heterogeneous network (VHetNet) architecture appears to be inevitable due to its high inherent agility. Moreover, employing an intelligent framework is another crucial requirement for self-evolving networks to deal with real-time network optimization problems. Hence, in this work, to provide a better insight on network architecture design in support of self-evolving networks, we highlight the merits of integrated VHetNet architecture while proposing an intelligent framework for self-evolving integrated vertical heterogeneous networks (SEI-VHetNets). The impact of the challenges associated with SEI-VHetNet architecture, on network management is also studied considering a generalized network model. Furthermore, the current literature on network management of integrated VHetNets along with the recent advancements in artificial intelligence (AI)/machine learning (ML) solutions are discussed. Accordingly, the core challenges of integrating AI/ML in SEI-VHetNets are identified. Finally, the potential future research directions for advancing the autonomous and self-evolving capabilities of SEI-VHetNets are discussed.Comment: 25 pages, 5 figures, 2 table