Security Of Unmanned Aerial Vehicles Research Articles

Fuzzy based trusted malicious unmanned aerial vehicle detection using in flying ad-hoc network

The communication security of unmanned aerial vehicles (UAVs) or drones based on flying ad hoc networks (FANETs) can be enhanced and maintained by assessing vulnerabilities, threats, and attacks. Thus, trust, high mobility, and complex communication interfaces are crucial for effective coordination and segregation of malicious drones from the genuine. A new efficient honesty-based detection scheme for malicious drones has been proposed to distinguish between intentional and unintentional misbehavior of the UAVs. The proposed scheme configures a drone system for packet transmission within the FANET, specifying buffer size and packet size variables to control data flow and prevent congestion. It then computes energy used for packet reception and transmission, optimizes energy consumption, and evaluates the drone’s mobility through the link stability index (LSI) and honesty UAV. The honesty UAV parameter categorizes UAVs into unintentional malicious and intentional malicious categories. Fuzzy logic helps identify intentional and unintentional misbehaving drones and improves performance in the network. It continuously generates the malicious UAV detection mechanism by assessing the parameters i.e., energy, pattern of movements, and the honesty score about the UAVs as a whole. Thereby a dynamic rating system to adeptly identify and differentiate the cooperative and non-cooperative nature of drones from the network is assimilated in the paper. Simulation results show the proposed scheme approximately aligns with the actual cases for varying numbers of malicious drones. Finally, the observation of the simulation result reflects substantial enhancement in performance metrics, with a significant cutback in end to end delay and packet delivery ratio by 10–30% and 20–50% respectively, contrasting with the existing techniques.

Exhaustive distributed intrusion detection system for UAVs attacks detection and security enforcement (E-DIDS)

Distributed intrusion detection systems (DIDS) are a specialized subset of conventional IDSs designed for implementation in distributed environments. Each IDS is integrated into distinct entities within a monitored network, potentially distributed across various locations. These participating IDSs can be configured to detect either a particular or multiple attack types. Although DIDS has found extensive application in diverse IoT systems, its utilization in unmanned aerial vehicles (UAVs) still needs to be explored. Consequently, it is imperative to devise a comprehensive framework tailored explicitly for UAVs. It combines multiple detection units to enhance security. Based on the insights gained from previous studies, we propose an exhaustive DIDS for UAVs security enforcement in this paper. Our proposed solution offers a robust and scalable security approach. Through distributing the workload across interconnected IDSs deployed on the UAV, our solution was optimized for UAVs attacks detection to achieve high detection performance while reducing the complexity. To the best of our insight, there is no recorded DIDS for UAVs security, and attack detection has been proposed and evaluated. Furthermore, our paper provides a detailed analysis, outlining the development basis and the achieved results. We performed multiple experiments over different cases using different datasets. The achieved experimental results demonstrate that the proposed IDS has significantly high accuracy detection and low loss rates. Our proposed E-DIDS efficiently detects multiple attacks on different UAVs subsets with good global accuracy that reached 98.6% and low resource consumption.

UAV Detection Using Reinforcement Learning.

Unmanned Aerial Vehicles (UAVs) have gained significant popularity in both military and civilian applications due to their cost-effectiveness and flexibility. However, the increased utilization of UAVs raises concerns about the risk of illegal data gathering and potential criminal use. As a result, the accurate detection and identification of intruding UAVs has emerged as a critical research concern. Many algorithms have shown their effectiveness in detecting different objects through different approaches, including radio frequency (RF), computer vision (visual), and sound-based detection. This article proposes a novel approach for detecting and identifying intruding UAVs based on their RF signals by using a hierarchical reinforcement learning technique. We train a UAV agent hierarchically with multiple policies using the REINFORCE algorithm with entropy regularization term to improve the overall accuracy. The research focuses on utilizing extracted features from RF signals to detect intruding UAVs, which contributes to the field of reinforcement learning by investigating a less-explored UAV detection approach. Through extensive evaluation, our findings show the remarkable results of the proposed approach in achieving accurate RF-based detection and identification, with an outstanding detection accuracy of 99.7%. Additionally, our approach demonstrates improved cumulative return performance and reduced loss. The obtained results highlight the effectiveness of the proposed solution in enhancing UAV security and surveillance while advancing the field of UAV detection.

ESUAV-NI: Endogenous Security Framework for UAV Perception System Based on Neural Immunity

Unmanned aerial vehicles (UAV) represent an essential component of advanced intelligent equipment that can be used as an aerial perception system by installing various sensors such as vision, hearing, touch, taste and smell to achieve intelligently integrated perception of environments. However, these perception system with environmental information may be threatened by various internal and external attacks, causing a great challenge to the security of UAV. The original security system relied on an expert knowledge base to prevent attacks, but the weaknesses of lacking proactivity and flexibility are gradually exposed. The strong resistance and survivability of biological systems can be used to fill this capability gap and provide new ideas for the security of UAV perception system. Therefore, an endogenous security framework (ESUAV-NI) based on Neural System (NS) and Immune System (IS) is proposed in this study. Through breeding artificial intelligence (AI) vaccines and distributed neural hierarchical control, achieving security protection for UAV perception system. Moreover, we evaluated the AI vaccine breeding approach in the ESUAV-NI by conducting extensive experiments on internal threats and external aerial imagery camouflage data, respectively. The results show that the proposed approach has superior performance for UAV perception system.

Hybrid cyber security of unmanned aerial vehicles

Unmanned aerial vehicle technological improvement has had a direct influence on today's warfare. As today's battles take place in cities, the distinction between civilians and soldiers has grown increasingly hazy. There is a wealth of literature on autonomy and ethical concerns concerning autonomous weapon systems. Unmanned aerial vehicles must contend with a slew of security risks, ranging from electronic jammers to missile strikes. The societal acceptance of these techniques, ethical value judgments, and long-term consequences are all important topics to discuss. Unmanned aerial vehicles have a wide range of applications in the military, from gathering intelligence to destroying targets, and in the civilian arena, from agriculture to photography. Furthermore, unmanned aerial vehicles have the potential to be used not just for military or commercial goals, but also for international crimes. Nowadays unmanned aerial vehicles are used in electronic warfare to monitor enemy positions, prevent their communications by jamming systems, instantly monitor the country's border security, render the electronic systems of the determined targets inoperable, and listen for information via communication systems. Under the conditions specified above, the study seeks to provide a hybrid approach to unmanned aerial vehicles. The cybersecurity of unmanned aerial vehicles, software security, physical security, and social and cultural security will all be handled from a single point of view.

An approach to enhance the security of unmanned aerial vehicles (UAVs)

An approach to enhance the security of unmanned aerial vehicles (UAVs)

Cyber4Drone: A Systematic Review of Cyber Security and Forensics in Next-Generation Drones

Cyber Security and forensics for Unmanned Aerial Vehicles (UAVs) pose unique requirements, solutions, and challenges. As UAVs become increasingly prevalent for legitimate and illegal use, ensuring their security and data integrity is important. Solutions have been developed to tackle these security requirements. Drone forensics enables the investigation of security incidents involving UAVs, aiding in identifying attackers or determining the cause of accidents. However, challenges persist in the domain of UAV security and forensics. This paper surveys drone threat models, security, and privacy aspects. In particular, we present the taxonomy of drone forensics for investigating drone systems and talk about relevant artifacts, tools, and benchmark datasets. While solutions exist, challenges such as evolving technology and complex operational environments must be addressed through collaboration, updated protocols, and regulatory frameworks to ensure drones’ secure and reliable operation. Furthermore, we also point out the field’s difficulties and potential future directions.

Employing automotive security to improve the security of unmanned aerial vehicles

The concern about cyber threats on unmanned aerial vehicles (UAVs) increases as they become more sophisticated and widely used in various areas. Although there have been efforts to improve the security of UAVs, their focus is limited to physical attacks and securing communication, and many areas remain open to issues. Although a more comprehensive approach is required to improve the overall areas of UAVs, it is reasonable to investigate the case study from the perspective of other similar cyber-physical systems (CPSs). Thus, we see the architectural similarity between UAVs and automobiles. Automotive security has seen significant improvement throughout the last decade as vehicles have begun to have connectivity and autonomy. There have been extensive research and development efforts in various aspects, including securing the components, securing communications, securing software updates, and securing the overall management system to provide multi-layered security for the automotive environments, and standardization and regulations are being issued. Due to the similarity, the employment of ideas from the automotive environments in the UAV environments becomes a reasonable approach. In this paper, we show the automotive security trends and discuss how UAV environments are comparable to automotive environments. We then show how security in automotives is adapted in UAVs with a case study.

DP-Authentication: A novel deep learning based drone pilot authentication scheme

Unmanned Aerial Vehicles (UAVs), also known as drones, have recently been proposed as flying base stations for providing reliable service to IoT devices. However, due to the lack of effective authentication schemes, UAVs are often hijacked by adversaries, which raises a high potential for sensitive information leakage. Therefore, designing a real-time authentication scheme is essential to enhance UAV safety. Up to the present, several works exist about pilot authentication by classifying radio-control signals. As propagating through the open environment, radio-control signals can be sniffed, analyzed, and simulated, posing significant threats to UAV security. For this reason, we propose a novel deep learning-based drone pilot authentication scheme, DP-Authentication, to protect UAVs from malicious radio-manipulated attacks. Specifically, we collect UAV flight data from the onboard PX4 flight stack and feed them into the authentication scheme to validate pilot legal status dynamically. As verified by comprehensive experiments, the proposed authentication scheme can authenticate pilots with an accuracy of 95.24% and detect malicious hijacking with an accuracy of 96.82%. Thanks to the low system overhead, it holds great promise for deployment on the UAV side to monitor pilot legal status in real-time.

A hybrid security system for drones based on ICMetric technology.

Recently, the number of drones has increased, and drones' illegal and malicious use has become prevalent. The dangerous and wasteful effects are substantial, and the probability of attacks is very high. Therefore, an anomaly detection and protection system are needed. This paper aims to design and implement an intelligent anomaly detection system for the security of unmanned aerial vehicles (UAVs)/drones. The proposed system is heavily based on utilizing ICMetric technology to exploit low-level device features for detection. This technology extracts the accelerometer and gyroscope sensors' bias to create a unique number known as the ICMetric number. Hence, ICMetric numbers represent additional features integrated into the dataset used to detect drones. This study performs the classification using a deep neural network (DNN). The experimental results prove that the proposed system achieves high levels of detection and performance metrics.

GENERALIZED MODEL OF THE ADS-B UNMANNED AERIAL VEHICLE DATA TRANSMISSION PROCESS IN A STEGANOGRAPHIC SYSTEM

The subject of the article is a model of the ADS-B data transmission process of an unmanned aerial vehicle in a steganographic system using direct spectrum expansion technology. The aim of the publication is to improve the security of unmanned aerial vehicles with an integrated ADS-B system. Particular scientific tasks: analysis of basic methods of ADS-B format data protection; development of the scheme of ADS-B drone data transfer in steganographic system with the use of direct spectrum expansion technology; improvement of the model of ADS-B drone data transfer in steganographic system with the use of direct spectrum expansion technology; determination of qualitative and quantitative characteristics as well as security properties of ADS-B format data. The following research results were obtained: as the result of the scientific works analysis the hypothesis about the perspectives of the ADS-B format steganographic data protection usage was put forward; the scheme of an unmanned aerial vehicle ADS-B data transmission in the steganographic system with the usage of the direct spectrum spreading technology was developed; the main safety properties as well as the safety parameters and characteristics of the ADS-B format information signal were formulated; the generalized model of an unmanned aerial vehicle ADS-B data transmission was further developed. This will improve the safety of UAVs. The advantages and disadvantages of the model were revealed, which allowed to determine the priority of further research and possible promising ways of solving the assigned tasks.

COMPARATIVE STUDIES OF METHODS FOR IMPROVING THE CYBER SECURITY OF UNMANNED AERIAL VEHICLES WITH THE BUILT-IN ADS-B SYSTEM

The subject of study in the article is methods for improving the cybersecurity of unmanned aerial vehicles with an integrated ADS-B system. The aim is to analyze and compare methods for improving the cybersecurity of unmanned aerial vehicles with an embedded ADS-B system. Particular scientific tasks: analysis and comparative studies of the main types of cyber threats and methods for improving the cybersecurity of unmanned aerial vehicles with an integrated ADS-B system, the development of an appropriate taxonomy, and the development of proposals for further research. The methods used in the article are the methods of system analysis. The following research results have been obtained: a classification of cyber threats to the security of unmanned aerial vehicles with an integrated ADS-B system has been developed; a taxonomy of ADS-B message modification and injection attacks has been developed; practical UAV security metrics adapted to the presented taxonomy of cyberattacks are defined and researched ; chosen most priority directions research ; - a generalized classification of cryptographic protection methods adapted to solve the problems of improving the security of UAVs with an integrated ADS-B system has been developed; the use of additional means of identification based on steganographic methods of data protection is proposed. Conclusions. An analysis and comparative studies have been carried out, and a generalized classification of methods for improving the cybersecurity of unmanned aerial vehicles with an integrated ADS-B system has been developed. Their advantages and disadvantages are revealed, which made it possible to determine the priority of further research and possible promising ways to solve the tasks.

A survey of cyber security threats and solutions for UAV communications and flying ad-hoc networks

Unmanned aerial vehicles (UAVs) are a rapidly evolving technology, and being highly mobile, UAV systems are able to cooperate with each other to accomplish a wide range of different tasks. UAVs can be used in commercial applications, such as goods delivery, as well as in military surveillance. They can also operate in civil domains like search-and-rescue missions, that require multiple UAVs to collect location data as well as transmit video streams. However, the malicious use of UAVs began to emerge in recent years. The frequency of such attacks has been significantly increasing and their impact can have devastating effects. Hence, the relevant industries and standardisation bodies are exploring possibilities for securing UAV systems and networks.Our survey focuses on UAV security and privacy issues whilst establishing flying ad-hoc networks (FANETs) as well as on threats to the Internet of drones (IoD) infrastructure used to provide control and access over the Internet between UAVs and users. The goal of this survey is to categorise the versatile aspects of the UAV threat landscape and develop a classification approach based on different types of connections and nodes in FANETs and IoD. In particular, we categorise security and privacy threats on connections between UAVs, ground control stations, and personal pilot devices. All the most relevant threats and their corresponding defence mechanisms are classified using characteristics of the first four layers of the OSI model. We then analyse the conventional and novel UAV routing protocols, indicating their advantages and disadvantages from the cyber security perspective. To provide a deeper insight, the reviewed defence mechanisms have undergone a thorough examination of their security requirements and objectives such as availability, authentication, authorisation, confidentiality, integrity, privacy, and non-repudiation. Finally, we discuss the open research challenges, the limitations of current UAV standards, and provide possible future directions for research.

Time-Division Multiarray Beamforming for UAV Communication

Recently, unmanned aerial vehicles (UAVs) have been widely used in various industries. However, the communication links of UAVs are also threatened by eavesdropping. To enhance physical layer security (PLS) for UAV communications, a time-division multiarray beamforming (TDMB) scheme is proposed. Multiple antenna arrays steer their beamforming vectors based on their position relative to the legitimate user (LU). Thus, angle-distance-dependent directional modulation (DM) can be achieved. Time-division means multiple antenna arrays take turns transmitting different symbols from a same packet. The receiver in undesired directions suffers from intersymbol interference (ISI) because of the path differences between the receiver and different antenna arrays. This paper shows the signal-to-interference-plus-noise ratio (SINR) distribution with the proposed method in a 2-dimensional plane. Also, the improvement of the secrecy rate with the proposed method under different total antennas and artificial noise power is studied. Overall, these results indicate that the security rate has improved more with the proposed method, where the numbers of antennas and the power of AN are limited. Therefore, this method is suitable for UAV security communication.

Investigation on vulnerabilities, threats and attacks prohibiting UAVs charging and depleting UAVs batteries: Assessments & countermeasures

Unmanned aerial vehicles (UAVs) are the current leading devices used by different sectors. They open doors to new opportunities for investors and business service providers. Their exponential growth turns the visibility of attackers to a new target to implement attacks. Researchers aim to fend off these attacks to empower UAVs security. Based on this motivation, this survey stands for UAVs attacks assessments against the based-system and the charging system. Also, the paper highlights UAVs battery depletion attacks. Furthermore, the paper presents different mitigations that stand against UAVs battery depletion attacks and UAVs charging system attacks. Besides, related research projects are explored.

Advancements in UAVs-A review.

Present decade has been observed that Unmanned Aerial Vehicles(UAVs) are occupying the sky to accomplish different missions. They became the popular area of interest due to their growing utility and relative cost. Increased demand of UAVs allowing the researchers for the optimized UAV as a major area of interest. They are providing new and better opportunities to cellular networks. UAV industry is undergoing a huge amount of advancements in it such as in navigation system, privacy and security ,mission management system, power system communication system etc. this article mainly focuses on the advancements regarding privacy and security of UAVs ,mission management system. The motivation behind the development of strategies for defending the breaches caused by data protection and public safety them are examined. This study gives an overview about past and how the present usage of UAVs is optimized by implementing the defense techniques. The major six criteria implemented to defend socio-technical concerns are explained. Few countries which adapted these regulations are discussed briefly .Ensuring security of the UAV system is studied considering physical security and cyber security as major aspects. The study also provides the techniques to defend these security threats. on Mission planning and mission management.

Security Threats and Countermeasures of Unmanned Aerial Vehicle Communications

Unmanned aerial vehicles (UAVs) have been widely applied in various fields, including but not limited to military, industry, and agriculture. But UAVs also confront severe security threats, which slows down their wide application. Current research mainly focuses on the security of a single UAV system, while paying little attention to UAV-related communications, especially in the physical and network layers. Thus, it becomes necessary to summarize potential security threats and corresponding countermeasures. To this end, we study mainstream attacks on UAV communications in order to propose security requirements. Then we present a comprehensive review on existing security countermeasures for enhancing UAV communication security in both the physical and network layers. We conclude with open issues and future prospects of UAV security.

Aspects of Equipping Transport Facilities with Systems for Controlling Routes of Unmanned Aerial Vehicles

Currently, the problem of ensuring safety of flights of unmanned aerial vehicles (UAVs) over the territory of transport infrastructure facilities (TIF), primarily airports, remains topical.In one of the previous works, the author together with the co-author proposed a method for increasing safety of movement of unmanned aerial vehicles and the system for controlling the routes of unmanned aerial vehicles (hereinafter – route control system, RCS) that implements it, which makes it possible to improve safety and security of UAV traffic at transport infrastructure facilities by limiting UAV traffic area strictly to a dedicated air corridor (DAC). The development of this system creates the prerequisites for removing the existing restrictions on the use of unmanned aerial vehicles at transport infrastructure facilities.For practical implementation of the proposed system, it is relevant to develop a method of placing RCS at transport infrastructure facilities. This condition can be justified by the fact that RCS, as a rule, will be located under conditions of dense infrastructural development, including dangerous technical elements of technical equipment, a collision of a UAV with which can lead to an emergency situation (ES); besides, the movement of air/ground vehicles will be carried out in the immediate vicinity of RCS along transport routes/corridors, and employees, passengers and visitors of TIF will move along the pedestrian paths.The objective of this study – to develop a methodology for placing systems controlling routes of unmanned aerial vehicles at transport infrastructure facilities.The study conducted with well-known scientific methods, including the basic routing problem posed by Dantzig and Ramser, modelling, analysis, and synthesis, made it possible to develop a method for placing RCS for unmanned aerial vehicles at transport infrastructure facilities. The practical application of the proposed methodology makes it possible to build routes for movement of UAV at TIF, to form a network of dedicated air corridors for UAV at TIF operated in relationship with the system, determine the optimal location of the RCS elements at TIF.

Intrusion Detection of UAVs Based on the Deep Belief Network Optimized by PSO.

With the rapid development of information technology, the problem of the network security of unmanned aerial vehicles (UAVs) has become increasingly prominent. In order to solve the intrusion detection problem of massive, high-dimensional, and nonlinear data, this paper proposes an intrusion detection method based on the deep belief network (DBN) optimized by particle swarm optimization (PSO). First, a classification model based on the DBN is constructed, and the PSO algorithm is then used to optimize the number of hidden layer nodes of the DBN, to obtain the optimal DBN structure. The simulations are conducted on a benchmark intrusion dataset, and the results show that the accuracy of the DBN-PSO algorithm reaches 92.44%, which is higher than those of the support vector machine (SVM), artificial neural network (ANN), deep neural network (DNN), and Adaboost. It can be seen from comparative experiments that the optimization effect of PSO is better than those of the genetic algorithm, simulated annealing algorithm, and Bayesian optimization algorithm. The method of PSO-DBN provides an effective solution to the problem of intrusion detection of UAV networks.

Software-Defined Networking for Unmanned Aerial Vehicular Networking and Security: A Survey

Despite the immense benefits offered by the utilization of unmanned aerial vehicles (UAVs) in civilian and military applications, significant work needs to be done to ensure that these systems are able to securely communicate and resiliently operate to accomplish the mission. As the UAVs grow with their popularity and usability for different applications, there is a dire need to ensure that UAVs and their networks are capable of mitigating cyber-attacks on the fly. One approach that has gained considerable popularity is Software-Defined Networking (SDN) based solutions. SDN is a networking paradigm that has gained attention due to its dynamic flexibility to program networks and increase network visibility, and its potential to assist in the mitigating security vulnerabilities in the network including the network of UAVs. This article provides an overview of recent advances, and current state of art related to security vulnerabilities and SDN enabled countermeasures. This paper also presents a comparison of different approaches in a tabular form and a discussion of challenges and future research directions with respect to UAV security.