Biblio

In the security platform of Internet of Things (IoT), a licensed Low Power Wide Area Network (LPWAN) technology, named Narrowband Internet of Things (NB-IoT) is playing a vital role in transferring the information between objects. This technology is preferable for applications having a low data rate. As the number of subscribers increases, attack possibilities raise simultaneously. So securing the transmission between the objects becomes a big task. Bandwidth spoofing is one of the most sensitive attack that can be performed on the communication channel that lies between the access point and user equipment. This research proposal objective is to secure the system from the attack based on Unmanned Aerial vehicles (UAVs) enabled Small Cell Access (SCA) device which acts as an intruder between the user and valid SCA and investigating the scenario when any intruder device comes within the communication range of the NB-IoT enabled device. Here, this article also proposed a mathematical solution for the proposed scenario.

Cognitive radio (CR) as a key technology of solving the problem of low spectrum utilization has attracted wide attention in recent years. However, due to the open nature of the radio, the communication links can be eavesdropped by illegal user, resulting to severe security threat. Unmanned aerial vehicle (UAV) equipped with signal sensing and data transmission module, can access to the unoccupied channel to improve network security performance by transmitting artificial noise (AN) in CR networks. In this paper, we propose a resource allocation scheme for UAV-assisted overlay CR network. Based on the result of spectrum sensing, the UAV decides to play the role of jammer or secondary transmitter. The power splitting ratio for transmitting secondary signal and AN is introduced to allocate the UAV's transmission power. Particularly, we jointly optimize the spectrum sensing time, the power splitting ratio and the hovering position of the UAV to maximize the total secrecy rate of primary and secondary users. The optimization problem is highly intractable, and we adopt an adaptive inertia coefficient particle swarm optimization (A-PSO) algorithm to solve this problem. Simulation results show that the proposed scheme can significantly improve the total secrecy rate in CR network.

We consider that in order to improve the utilization rate of spectrum resources and the security rate of unmanned aerial vehicle (UAV) Communication system, a secure transmission scheme of UAV relay assisted cognitive radio network (CRN) is proposed. In the presence of primary users and eavesdroppers, the UAV acts as the decoding and forwarding mobile relay to assist the secure transmission from the source node to the legitimate destination node. This paper optimizes the flight trajectory and transmission power of the UAV relay to maximize the security rate. Since the design problem is nonconvex, the original problem is approximated to a convex constraint by constructing a surrogate function with nonconvex constraints, and an iterative algorithm based on continuous convex approximation is used to solve the problem. The simulation results show that the algorithm can effectively improve the average security rate of the secondary system and successfully optimize the UAV trajectory.

With advantages of low cost, high mobility, and flexible deployment, unmanned aerial vehicle (UAVs) are employed to efficiently detect abnormal vehicle behaviors (AVBs) in the internet of vehicles (IoVs). However, due to limited resources including battery, computing, and communication, UAVs are selfish to work cooperatively. To solve the above problem, in this paper, a game theoretical UAV incentive scheme in IoVs is proposed. Specifically, the abnormal behavior model is first constructed, where three model categories are defined: velocity abnormality, distance abnormality, and overtaking abnormality. Then, the barging pricing framework is designed to model the interactions between UAVs and IoVs, where the transaction prices are determined with the abnormal behavior category detected by UAVs. At last, simulations are conducted to verify the feasibility and effectiveness of our proposed scheme.

In emergency situations like recent Australian bushfires, it is crucial for civilians and firefighters to receive critical information such as escape routes and safe sheltering points with guarantees on information quality attributes. Mobile Ad-hoc Networks (MANETs) can provide communications in bushfire when fixed infrastructure is destroyed and not available. Current MANET solutions, however, are mostly tested under static bushfire scenario. In this work, we investigate the impact of a realistic dynamic bushfire in a dry eucalypt forest with a shrubby understory, on the performance of data delivery solutions in a MANET. Simulation results show a significant degradation in the performance of state-of-the-art MANET quality of information solution. Other than frequent source handovers and reduced user usability, packet arrival latency increases by more than double in the 1st quartile with a median drop of 74.5 % in the overall packet delivery ratio. It is therefore crucial for MANET solutions to be thoroughly evaluated under realistic dynamic bushfire scenarios.

Nowadays, an increasing trend to use autonomous Unmanned Aerial Vehicles (UAV) for applications like logistics as well as security and surveillance can be recorded. Autonomic UAVs require robust and precise navigation to ensure efficient and safe operation even in strong multipath environments and (intended) interference. The need for robust navigation on UAVs implies the necessary integration of low-cost, lightweight, and compact array antennas as well as structures for multipath mitigation into the UAV platform. This article investigates a miniaturized antenna array mounted on top of vertical choke rings for robust navigation purposes. The array employs four 3D printed elements based on dielectric resonators capable of operating in all GNSS bands while compact enough for mobile applications such as UAV.

A non-predicted and transient malfunctioning of one or multiple unmanned aerial vehicles (UAVs) is something that may happen over a course of their deployment. Therefore, it is very important to have means to detect these events and take actions for ensuring a high level of reliability, security, and safety of the flight for the predefined mission. In this research, we propose algorithms aiming at the detection and isolation of any faulty UAV so that the performance of the UAVs application is kept at its highest level. To this end, we propose the use of Kullback-Leiler Divergence (KLD) and Artificial Neural Network (ANN) to build algorithms that detect and isolate any faulty UAV. The proposed methods are declined in these two directions: (1) we compute a difference between the internal and external data, use KLD to compute dissimilarities, and detect the UAV that transmits erroneous measurements. (2) Then, we identify the faulty UAV using an ANN model to classify the sensed data using the internal sensed data. The proposed approaches are validated using a real dataset, provided by the Air Lab Failure and Anomaly (ALFA) for UAV fault detection research, and show promising performance.

Experienced developers of safety-critical and security-critical systems have long emphasized the importance of applying the highest degree of scrutiny to a system's I/O boundaries. From a safety perspective, input validation is a traditional “best practice.” For security-critical architecture and design, identification of the attack surface has emerged as a primary analysis technique. One of our current research focus areas concerns the identification of and mitigation against attacks along that surface, using mathematically-based tools. We are motivated in these efforts by emerging application areas, such as assured autonomy, that feature a high degree of network connectivity, require sophisticated algorithms and data structures, are subject to stringent accreditation/certification, and encourage hardware/software co-design approaches. We have conducted several experiments employing a state-of-the-art toolchain, due to Russinoff and O'Leary, and originally designed for use in floating-point hardware verification, to determine its suitability for the creation of safety-critical/security-critical input filters. We focus first on software implementation, but extending to hardware as well as hardware/software co-designs. We have implemented a high-assurance filter for JSON-formatted data used in an Unmanned Aerial Vehicle (UAV) application. Our JSON filter is built using a table-driven lexer/parser, supported by mathematically-proven lexer and parser table generation technology, as well as verified data structures. Filter behavior is expressed in a subset of Algorithmic C, which defines a set of C++ header files providing support for hardware design, including the peculiar bit widths utilized in that discipline, and enables compilation to both hardware and software platforms. The Russinoff-O'Leary Restricted Algorithmic C (RAC) toolchain translates Algorithmic C source to the Common Lisp subset supported by the ACL2 theorem prover; once in ACL2, filter behavior can be mathematically verified. We describe how we utilize RAC to translate our JSON filter to ACL2, present proofs of correctness for its associated data types, and describe validation and performance results obtained through the use of concrete test vectors.

As the unmanned aerial vehicle (UAV) can play a vital role to collect information remotely in a military battlefield, researchers have shown great interest to reveal the domain of internet of battlefield Things (IoBT). In a rescue mission on a battlefield, UAV can collect data from different regions to identify the casualty of a soldier. One of the major challenges in IoBT is to identify the soldier in a complex environment. Image processing algorithm can be helpful if proper methodology can be applied to identify the victims. However, due to the limited hardware resources of a UAV, processing task can be handover to the nearby edge computing server for offloading the task as every second is very crucial in a battlefield. Furthermore, to avoid any third-party interaction in the network and to store the data securely, blockchain can help to create a trusted network as it forms a distributed ledger among the participants. This paper proposes a UAV assisted casualty detection scheme based on image processing algorithm where data is protected using blockchain technology. Result analysis has been conducted to identify the victims on the battlefield successfully using image processing algorithm and network issues like throughput and delay has been analyzed in details using public-key cryptography.

As the increasing demands of social services, unmanned aerial vehicles (UAVs)-assisted networks promote the promising prospect for implementing high-rate information transmission and applications. The sensing data can be collected by UAVs, a large number of applications based on UAVs have been realized in the 5G networks. However, the malicious UAVs may provide false information and destroy the services. The 5G UAV communication systems face the security threats. Therefore, this paper develops a novel trust-based security scheme for 5G UAV communication systems. Firstly, the architecture of the 5G UAV communication system is presented to improve the communication performance. Secondly, the trust evaluation scheme for UAVs is developed to evaluate the reliability of UAVs. By introducing the trust threshold, the malicious UAVs will be filtered out from the systems to protect the security of systems. Finally, the simulation results have been demonstrated the effectiveness of the proposed scheme.

Unmanned aerial vehicles (UAVs) based cognitive radio (CR) systems improve the sensing performance. However, such systems demand secure communication with lower power consumption. Motivated by these observations, we consider an energy-constraint yet energy harvesting (EH) drone flying periodically in the circular track around primary transmitter in the presence of an eavesdropper with an aim to use the licensed band opportunistically. Considering the trade-off between the residual energy and secondary link performance, we formulate the constrained optimization problem, i.e., maximizing residual energy under the constraint of secondary secrecy outage. Simulation results verify the proposed theoretical analysis.

We use potential fields tuned by genetic algorithms to dynamically reconFigure unmanned aerial vehicles networks to serve user bandwidth needs. Such flying network base stations have applications in the many domains needing quick temporary networked communications capabilities such as search and rescue in remote areas and security and defense in overwatch and scouting. Starting with an initial deployment that covers an area and discovers how users are distributed across this area of interest, tuned potential fields specify subsequent movement. A genetic algorithm tunes potential field parameters to reposition UAVs to create and maintain a mesh network that maximizes user bandwidth coverage and network lifetime. Results show that our evolutionary adaptive network deployment algorithm outperforms the current state of the art by better repositioning the unmanned aerial vehicles to provide longer coverage lifetimes while serving bandwidth requirements. The parameters found by the genetic algorithm on four training scenarios with different user distributions lead to better performance than achieved by the state of the art. Furthermore, these parameters also lead to superior performance in three never before seen scenarios indicating that our algorithm finds parameter values that generalize to new scenarios with different user distributions.

Unmanned Aerial Vehicles (UAVs) also known as drones are being used in many applications where they can record or stream videos. One interesting application is the Intelligent Transportation Systems (ITS) and public safety applications where drones record videos and send them to a control center for further analysis. These videos are shared by various clients such as law enforcement or emergency personnel. In such cases, the recording might include faces of civilians or other sensitive information that might pose privacy concerns. While the video can be encrypted and stored in the cloud that way, it can still be accessed once the keys are exposed to third parties which is completely insecure. To prevent such insecurity, in this paper, we propose proxy re-encryption based sharing scheme to enable third parties to access only limited videos without having the original encryption key. The costly pairing operations in proxy re-encryption are not used to allow rapid access and delivery of the surveillance videos to third parties. The key management is handled by a trusted control center, which acts as the proxy to re-encrypt the data. We implemented and tested the approach in a realistic simulation environment using different resolutions under ns-3. The implementation results and comparisons indicate that there is an acceptable overhead while it can still preserve the privacy of drivers and passengers.

With the emergence of new digital trends like Internet of Things (IoT), more industry actors and technical committees pursue research in utilising such technologies as they promise a better and optimised management, improved energy efficiency and a better quality living through a wide array of value-added services. However, as sensing, actuation, communication and control become increasingly more sophisticated, such promising data-driven systems generate, process, and exchange larger amounts of security-critical and privacy-sensitive data, which makes them attractive targets of attacks. In turn this affirms the importance of trustworthiness in IoT and emphasises the need of a solid technical and regulatory foundation. The goal of this paper is to first introduce the concept of trustworthiness in IoT, its main pillars namely, security, privacy and data protection, and then analyse the state-of-the-art in research and standardisation for each of these subareas. Throughout the paper, we develop and refer to Unmanned Aerial Vehicles (UAVs) as a promising value-added service example of mobile IoT devices. The paper then presents a thorough gap analysis and concludes with recommendations for future work.

Naval Battlefield Network communications rely on wireless network technologies to transmit data between different naval entities, such as ships and shore nodes. Existing naval battle networks heavily depend on the satellite communication system using single-path TCP for reliable, non-interactive data. While satisfactory for traditional use cases, this communication model may be inadequate for outlier cases, such as those arising from satellite failure and wireless signal outage. To promote network stability and assurance in such scenarios, the addition of unmanned aerial vehicles to function as relay points can complement network connectivity and alleviate potential strains in adverse conditions. The inherent mobility of aerial vehicles coupled with existing source node movements, however, leads to frequent network handovers with non-negligible overhead and communication interruption, particularly in the present single-path model. In this paper, we propose a solution based on multi-path TCP and software-defined networking, which, when applied to mobile wireless heterogeneous networks, reduces the network handover delay and improves the total throughput for transmissions among various naval entities at sea and littoral. In case of single link failure, the presence of a connectable relay point maintains TCP connectivity and reduces the risk of service interruption. To validate feasibility and to evaluate performance of our solution, we constructed a Mininet- WiFi emulation testbed. Compared against single-path TCP communication methods, execution of the testbed when configured to use multi-path TCP and UAV relays yields demonstrably more stable network handovers with relatively low overhead, greater reliability of network connectivity, and higher overall end-to-end throughput. Because the SDN global controller dynamically adjusts allocations per user, the solution effectively eliminates link congestion and promotes more efficient bandwidth utilization.

In this paper, we present results from a human-subject study designed to explore two facets of human mental models of robots - inferred capability and intention - and their relationship to overall trust and eventual decisions. In particular, we examine delegation situations characterized by uncertainty, and explore how inferred capability and intention are applied across different tasks. We develop an online survey where human participants decide whether to delegate control to a simulated UAV agent. Our study shows that human estimations of robot capability and intent correlate strongly with overall self-reported trust. However, overall trust is not independently sufficient to determine whether a human will decide to trust (delegate) a given task to a robot. Instead, our study reveals that estimations of robot intention, capability, and overall trust are integrated when deciding to delegate. From a broader perspective, these results suggest that calibrating overall trust alone is insufficient; to make correct decisions, humans need (and use) multi-faceted mental models when collaborating with robots across multiple contexts.

In recent years, unmanned aerial vehicles (UAVs) have become increasingly accessible to the public due to their high availability with affordable prices while being equipped with better technology. However, this raises a great concern from both the cyber and physical security perspectives since UAVs can be utilized for malicious activities in order to exploit vulnerabilities by spying on private properties, critical areas or to carry dangerous objects such as explosives which makes them a great threat to the society. Drone identification is considered the first step in a multi-procedural process in securing physical infrastructure against this threat. In this paper, we present drone detection and identification methods using deep learning techniques such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN) and Convolutional Recurrent Neural Network (CRNN). These algorithms will be utilized to exploit the unique acoustic fingerprints of the flying drones in order to detect and identify them. We propose a comparison between the performance of different neural networks based on our dataset which features audio recorded samples of drone activities. The major contribution of our work is to validate the usage of these methodologies of drone detection and identification in real life scenarios and to provide a robust comparison of the performance between different deep neural network algorithms for this application. In addition, we are releasing the dataset of drone audio clips for the research community for further analysis.

With the advancement of unmanned aerial vehicles (UAV), 3D wireless mesh networks will play a crucial role in next generation mission critical wireless networks. Along with providing coverage over difficult terrain, it provides better spectral utilization through 3D spatial reuse. However, being a wireless network, 3D meshes are vulnerable to jamming/disruptive attacks. A jammer can disrupt the communication, as well as control of the network by intelligently causing interference to a set of nodes. This paper presents a distributed mechanism of avoiding jamming attacks by means of 3D spatial filtering where adaptive beam nulling is used to keep the jammer in null region in order to bypass jamming. Kalman filter based tracking mechanism is used to estimate the most likely trajectory of the jammer from noisy observation of the jammer's position. A beam null border is determined by calculating confidence region of jammer's current and next position estimates. An optimization goal is presented to calculate optimal beam null that minimizes the number of deactivated links while maximizing the higher value of confidence for keeping the jammer inside the null. The survivability of a 3D mesh network with a mobile jammer is studied through simulation that validates an 96.65% reduction in the number of jammed nodes.

Unmanned Aerial Vehicles (UAVs) are autonomous aircraft that, when equipped with wireless communication interfaces, can share data among themselves when in communication range. Compared to single UAVs, using multiple UAVs as a collaborative swarm is considerably more effective for target tracking, reconnaissance, and surveillance missions because of their capacity to tackle complex problems synergistically. Success rates in target detection and tracking depend on map coverage performance, which in turn relies on network connectivity between UAVs to propagate surveillance results to avoid revisiting already observed areas. In this paper, we consider the problem of optimizing three objectives for a swarm of UAVs: (a) target detection and tracking, (b) map coverage, and (c) network connectivity. Our approach, Dual-Pheromone Clustering Hybrid Approach (DPCHA), incorporates a multi-hop clustering and a dual-pheromone ant-colony model to optimize these three objectives. Clustering keeps stable overlay networks, while attractive and repulsive pheromones mark areas of detected targets and visited areas. Additionally, DPCHA introduces a disappearing target model for dealing with temporarily invisible targets. Extensive simulations show that DPCHA produces significant improvements in the assessment of coverage fairness, cluster stability, and connection volatility. We compared our approach with a pure dual- pheromone approach and a no-base model, which removes the base station from the model. Results show an approximately 50% improvement in map coverage compared to the pure dual-pheromone approach.

Thanks to advancement of vehicle technologies, Unmanned Aerial Vehicle (UAV) now widely spread over practical services and applications affecting daily life of people positively. Especially, multiple heterogeneous UAVs with different capabilities should be considered since UAVs can play an important role in Internet of Things (IoT) environment in which the heterogeneity and the multi domain of UAVs are indispensable. Also, a concept of barrier-coverage has been proved as a promising one applicable to surveillance and security. In this paper, we present collision-free reinforced barriers by heterogeneous UAVs to support multi domain. Then, we define a problem which is to minimize maximum movement of UAVs on condition that a property of collision-free among UAVs is assured while they travel from current positions to specific locations so as to form reinforced barriers within multi domain. Because the defined problem depends on how to locate UAVs on barriers, we develop a novel approach that provides a collision-free movement as well as a creation of virtual lines in multi domain. Furthermore, we address future research topics which should be handled carefully for the barrier-coverage by heterogeneous UAVs.

Interconnected everyday objects, either via public or private networks, are gradually becoming reality in modern life - often referred to as the Internet of Things (IoT) or Cyber-Physical Systems (CPS). One stand-out example are those systems based on Unmanned Aerial Vehicles (UAVs). Fleets of such vehicles (drones) are prophesied to assume multiple roles from mundane to high-sensitive applications, such as prompt pizza or shopping deliveries to the home, or to deployment on battlefields for battlefield and combat missions. Drones, which we refer to as UAVs in this paper, can operate either individually (solo missions) or as part of a fleet (group missions), with and without constant connection with a base station. The base station acts as the command centre to manage the drones' activities; however, an independent, localised and effective fleet control is necessary, potentially based on swarm intelligence, for several reasons: 1) an increase in the number of drone fleets; 2) fleet size might reach tens of UAVs; 3) making time-critical decisions by such fleets in the wild; 4) potential communication congestion and latency; and 5) in some cases, working in challenging terrains that hinders or mandates limited communication with a control centre, e.g. operations spanning long period of times or military usage of fleets in enemy territory. This self-aware, mission-focused and independent fleet of drones may utilise swarm intelligence for a), air-traffic or flight control management, b) obstacle avoidance, c) self-preservation (while maintaining the mission criteria), d) autonomous collaboration with other fleets in the wild, and e) assuring the security, privacy and safety of physical (drones itself) and virtual (data, software) assets. In this paper, we investigate the challenges faced by fleet of drones and propose a potential course of action on how to overcome them.

With the rapid development of science and technology, unmanned aerial vehicles (UAVs) gradually become the worldwide focus of science and technology. Not only the development and application but also the security of UAV is of great significance to modern society. Different from methods using radar, optical or acoustic sensors to detect UAV, this paper proposes a novel distance-based support vector data description (SVDD) algorithm using hash fingerprint as feature. This algorithm does not need large number of training samples and its computation complexity is low. Hash fingerprint is generated by extracting features of signal preamble waveforms. Distance-based SVDD algorithm is employed to efficiently detect and recognize low, slow, small unmanned aerial vehicles (LSSUAVs) using 2.4GHz frequency band.