A Survey of Security in UAVs and FANETs: Issues, Threats, Analysis of Attacks, and Solutions

Thanks to the rapidly developing technology, unmanned aerial vehicles (UAVs) are able to complete a number of tasks in cooperation with each other without need for human intervention. In recent years, UAVs, which are widely utilized in military missions, have begun to be deployed in civilian applications and mostly for commercial purposes. With their growing numbers and range of applications, UAVs are becoming more and more popular; on the other hand, they are also the target of various threats which can exploit various vulnerabilities of UAV systems in order to cause destructive effects. It is therefore critical that security is ensured for UAVs and the networks that provide communication between UAVs. In this survey, we aimed to present a comprehensive detailed approach to security by classifying possible attacks against UAVs and flying ad hoc networks (FANETs). We classified the security threats into four major categories that make up the basic structure of UAVs; hardware attacks, software attacks, sensor attacks, and communication attacks. In addition, countermeasures against these attacks are presented in separate groups as prevention and detection. In particular, we focus on the security of FANETs, which face significant security challenges due to their characteristics and are also vulnerable to insider attacks. Therefore, this survey presents a review of the security fundamentals for FANETs, and also four different routing attacks against FANETs are simulated with realistic parameters and then analyzed. Finally, limitations and open issues are also discussed to direct future wor

Resources Efficient Dynamic Clustering Algorithm for Flying Ad-Hoc Network

Unmanned Aerial Vehicles, often known as UAVs, are connected in the form of a Flying Ad-hoc Network, or FANET, and placed to use in a variety of applications to carry out efficient remote monitoring. Their great mobility has an adverse effect on their energy consumption, which in turn has a detrimental effect on the network's stability and the effectiveness of communication. To manage the very dynamic flying behavior of UAVs and to keep the network stable, novel clustering algorithms have been implemented. In this context, a novel clustering technique is developed to meet the rapid mobility of UAVs and to offer safe inter-UAV distance, reliable communication, and an extended network lifespan. It also provides a detailed analysis of the similarities and distinctions between AODV, AOMDV, DSDV, and DumbAgent.The performance of these protocols is analyzed using the NS-2 simulator. The simulation results are shown in our study with AODV, AOMDV, DSDV, and DumbAgent. The results of the simulation make it abundantly evident that the AODV routing protocol outperforms the other routing protocols DSDV, AOMDV, and DumbAgent in terms of the number of packets lost, the amount of throughput achieved, the amount of routing overhead generated, and the amount of delay

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

Federated Deep Learning for Cyber Security in the Internet of Things: Concepts, Applications, and Experimental Analysis

In this article, we present a comprehensive study with an experimental analysis of federated deep learning approaches for cyber security in the Internet of Things (IoT) applications. Specifically, we first provide a review of the federated learning-based security and privacy systems for several types of IoT applications, including, Industrial IoT, Edge Computing, Internet of Drones, Internet of Healthcare Things, Internet of Vehicles, etc. Second, the use of federated learning with blockchain and malware/intrusion detection systems for IoT applications is discussed. Then, we review the vulnerabilities in federated learning-based security and privacy systems. Finally, we provide an experimental analysis of federated deep learning with three deep learning approaches, namely, Recurrent Neural Network (RNN), Convolutional Neural Network (CNN), and Deep Neural Network (DNN). For each deep learning model, we study the performance of centralized and federated learning under three new real IoT traffic datasets, namely, the Bot-IoT dataset, the MQTTset dataset, and the TON_IoT dataset. The goal of this article is to provide important information on federated deep learning approaches with emerging technologies for cyber security. In addition, it demonstrates that federated deep learning approaches outperform the classic/centralized versions of machine learning (non-federated learning) in assuring the privacy of IoT device data and provide the higher accuracy in detecting attacks

Blockchain-assisted UAV communication systems: a comprehensive survey

Unmanned aerial vehicles (UAVs) have recently established their capacity to provide cost-effective and credible solutions for various real-world scenarios. UAVs provide an immense variety of services due to their autonomy, mobility, adaptability, and communications interoperability. Despite the expansive use of UAVs to support ground communications, data exchanges in those networks are susceptible to security threats because most communication is through radio or Wi-Fi signals, which are easy to hack. While several techniques exist to protect against cyberattacks. Recently emerging technology blockchain could be one of promising ways to enhance data security and user privacy in peer-to-peer UAV networks. Borrowing the superiorities of blockchain, multiple entities can communicate securely, decentralized, and equitably. This article comprehensively overviews privacy and security integration in blockchain-assisted UAV communication. For this goal, we present a set of fundamental analyses and critical requirements that can help build privacy and security models for blockchain and help manage and support decentralized data storage systems. The UAV communication system's security requirements and objectives, including availability, authentication, authorization, confidentiality, integrity, privacy, and non-repudiation, are thoroughly examined to provide a deeper insight. We wrap up with a discussion of open research challenges, the constraints of current UAV standards, and potential future research directions

RF Jamming Classification Using Relative Speed Estimation in Vehicular Wireless Networks

Wireless communications are vulnerable against radio frequency (RF) interference which might be caused either intentionally or unintentionally. A particular subset of wireless networks, Vehicular Ad-hoc NETworks (VANET), which incorporate a series of safety-critical applications, may be a potential target of RF jamming with detrimental safety effects. To ensure secure communications between entities and in order to make the network robust against this type of attacks, an accurate detection scheme must be adopted. In this paper, we introduce a detection scheme that is based on supervised learning. e k-nearest neighbors (KNN) and random forest (RaFo) methods are used, including features, among which one is the metric of the variations of relative speed (VRS) between the jammer and the receiver. VRS is estimated from the combined value of the useful and the jamming signal at the receiver. e KNN-VRS and RaFo-VRS classification algorithms are able to detect various cases of denial-of-service (DoS) RF jamming attacks and differentiate those attacks from cases of interference with very high accuracy

Decentralized Federated Learning: Fundamentals, State-of-the-art, Frameworks, Trends, and Challenges

In the last decade, Federated Learning (FL) has gained relevance in training collaborative models without sharing sensitive data. Since its birth, Centralized FL (CFL) has been the most common approach in the literature, where a central entity creates a global model. However, a centralized approach leads to increased latency due to bottlenecks, heightened vulnerability to system failures, and trustworthiness concerns affecting the entity responsible for the global model creation. Decentralized Federated Learning (DFL) emerged to address these concerns by promoting decentralized model aggregation and minimizing reliance on centralized architectures. However, despite the work done in DFL, the literature has not (i) studied the main aspects differentiating DFL and CFL; (ii) analyzed DFL frameworks to create and evaluate new solutions; and (iii) reviewed application scenarios using DFL. Thus, this article identifies and analyzes the main fundamentals of DFL in terms of federation architectures, topologies, communication mechanisms, security approaches, and key performance indicators. Additionally, the paper at hand explores existing mechanisms to optimize critical DFL fundamentals. Then, the most relevant features of the current DFL frameworks are reviewed and compared. After that, it analyzes the most used DFL application scenarios, identifying solutions based on the fundamentals and frameworks previously defined. Finally, the evolution of existing DFL solutions is studied to provide a list of trends, lessons learned, and open challenges

Improving Security for the Internet of Things: Applications of Blockchain, Machine Learning and Inter-Pulse Interval

The Internet of Things (IoT) is a concept where physical objects of various sizes can seamlessly connect and communicate with each other without human intervention. The concept covers various applications, including healthcare, utility services, automotive/vehicular transportation, smart agriculture and smart city. The number of interconnected IoT devices has recently grown rapidly as a result of technological advancement in communications and computational systems. Consequently, this trend also highlights the need to address issues associated with IoT, the biggest risk of which is commonly known to be security. This thesis focuses on three selected security challenges from the IoT application areas of connected and autonomous vehicles (CAVs), Internet of Flying Things (IoFT), and human body interface and control systems (HBICS). For each of these challenges, a novel and innovative solution is proposed to address the identified problems. The research contributions of this thesis to the literature can be summarised as follows: • A blockchain-based conditionally anonymised pseudonym management scheme for CAVs, supporting multi-jurisdictional road networks. • A Sybil attack detection scheme for IoFT using machine learning carried out on intrinsically generated physical layer data of radio signals. • A potential approach of using inter-pulse interval (IPI) biometrics for frequency hopping to mitigate jamming attacks on HBICS devices

Unmanned Aerial Vehicle (UAV)-Enabled Wireless Communications and Networking

The emerging massive density of human-held and machine-type nodes implies larger traffic deviatiolns in the future than we are facing today. In the future, the network will be characterized by a high degree of flexibility, allowing it to adapt smoothly, autonomously, and efficiently to the quickly changing traffic demands both in time and space. This flexibility cannot be achieved when the network’s infrastructure remains static. To this end, the topic of UAVs (unmanned aerial vehicles) have enabled wireless communications, and networking has received increased attention. As mentioned above, the network must serve a massive density of nodes that can be either human-held (user devices) or machine-type nodes (sensors). If we wish to properly serve these nodes and optimize their data, a proper wireless connection is fundamental. This can be achieved by using UAV-enabled communication and networks. This Special Issue addresses the many existing issues that still exist to allow UAV-enabled wireless communications and networking to be properly rolled out