CERES :: Browsing by Author "Al-Rubaye, Saba"

Browsing by Author "Al-Rubaye, Saba"

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Open Access
5G aviation networks using novel AI approach for DDoS detection
(IEEE, 2023-07-17) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, Julia
The advent of Fifth Generation (5G) technology has ushered in a new era of advancements in the aviation sector. However, the introduction of smart infrastructure has significantly altered the threat landscape at airports, leading to an increased vulnerability due to the proliferation of endpoints. Consequently, there is an urgent requirement for an automated detection system capable of promptly identifying and thwarting network intrusions. This research paper proposes a deep learning methodology that merges a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) to effectively detect various types of cyber threats using tabular-based image data. To transform time series features into 2D texture images, Gramian Angular Fields (GAFs) are utilized. These images are then stacked to form an N-channel image, which is fed into the CNN-GRU architecture for sequence analysis and identification of potential threats. The provide solution GAF-CNN-GRU achieved an accuracy of 98.6% on the Cranfield Embedded Systems Attack Dataset. We further achieved Precision, Recall and F1-scores of 97.84%, 91% and 94.3%. To evaluate model robustness we further tested this approach, using a benchmark random selection of input features, on the Canadian Institute for Cyber-Security (CIC) 2019 Distributed Denial-of-service attack (DDoS) Dataset achieving an Accuracy of 89.08%. Following feature optimisation our approach was able to achieve an accuracy of 98.36% with Precision, Recall and F1 scores of 93.09%, 95.45% and 94.56% respectively.
Open Access
Adaptive detection tracking system for autonomous UAV maritime patrolling
(IEEE, 2020-08-06) Panico, Alessandro; Zanotti Fragonara, Luca; Al-Rubaye, Saba
Nowadays, Unmanned Aerial Vehicles (UAVs) are considered reliable systems, suitable for several autonomous applications, especially for target detection and tracking. Although significant developments were achieved in object detection systems over the last decades using the deep learning technique known as Convolutional Neural Networks (CNN), there are still research gaps in this area. In this paper, we present a new object detection-tracking algorithm that can be used on low power consuming processing boards. In particular, we analysed a specific application scenario in which a UAV patrols coastlines and autonomously classifies different kind of marine objects. Current state of the art solutions propose centralised architectures or flying systems with human in the loop, making the whole system poorly efficient and not scalable. On the contrary, applying a Deep Learning detection system that runs on commercial Graphics Processing Units (GPUs) makes UAVs potentially more efficient than humans (especially for dull tasks like coastline patrolling) and the whole system becomes easily scalable because each UAV can fly independently and the Ground Control Station does not represent a bottleneck. To deal with this task, a database consisting of more than 115000 images was created to train and test several CNN architectures. Furthermore, an adaptive detection-tracking algorithm was introduced to make the whole system faster by optimizing the balancing between detecting new objects and tracking existing targets. The proposed solution is based on the measure of the tracking confidence and the frame similarity, by means of the Structural SIMilarity (SSIM) index, computed both globally and locally. Finally, the developed algorithms were tested on a realistic scenario by means of a UAV test-bed.
Open Access
Adaptive intelligent traffic control systems in smart city
(Cranfield University, 2023-05) Ahmed, Aminag Hardwan B.; Al-Rubaye, Saba; Panagiotakopoulos, Dimitrios
Traffic congestion in urban areas presents a significant challenge with far-reaching impacts on the economy, environment, and overall quality of life. To address this challenge, this thesis proposes a novel approach to traffic signal control aimed at alleviating traffic congestion more effectively. The research problem this study explores is the design and implementation of an adaptive system for traffic signal control in urban road networks, specifically focused on how to effectively manage traffic signal timings to mitigate congestion. The major contributions of this study include the development of a unique coordination algorithm for adaptive traffic signal control, utilizing Multi- Agent Reinforcement Learning (MARL) and Ant Colony Optimization (ACO). This algorithm's uniqueness is reflected in its capacity to simulate the behavior of ant colonies to guide multiple agents in managing traffic signals at various intersections, enabling them to learn from their environment and interactions to optimize signal timings By simulating the behavior of ant colonies, the algorithm guides multiple agents in managing traffic signals at various network intersections, learning from their interactions with the environment and each other to optimize signal timings. This research sets out to address the challenge of traffic congestion in urban areas. With cities worldwide struggling with this issue, the task of managing traffic signal timings to reduce congestion is paramount. The problem formulation involved the exploration of how novel Machine Learning (ML) techniques, such as Multi-Agent Reinforcement Learning (MARL) and Ant Colony Optimization (ACO), could be utilized to develop an adaptive coordination algorithm for traffic signal control. These techniques were chosen due to their potential for learning and adapting over time to optimize signal timings based on ever-changing traffic conditions. The novelty of this research lies in the unique combination of MARL, Actor-Critic (AC), and ACO techniques to develop an adaptive coordination algorithm for traffic signal control. By integrating these techniques, we've created a system where multiple agents can independently control traffic signals at different intersections, learning from their surroundings and interactions to continually improve signal timings. This innovative use of ML, especially MARL and ACO, represents a significant contribution to the field of traffic management, as it offers the potential to adapt to changing traffic patterns and conditions in real-time. This adaptability is expected to lead to more efficient traffic flow and decreased congestion, outcomes not fully realized by existing fixed-time and traditional adaptive signal control methods.
Open Access
Adaptive UAV swarm mission planning by temporal difference learning
(IEEE, 2021-11-15) Gopalakrishnan, Shreevanth Krishnaa; Al-Rubaye, Saba; Inalhan, Gokhan
The prevalence of Unmanned Aerial Vehicles (UAVs) in precision agriculture has been growing rapidly. This paper tackles the UAV global mission planning problem by incorporating a greater capacity for human-machine teaming in the architecture of a flexibly autonomous, near-fully-distributed Mission Management System for UAV swarms. Subsequently, the two problems of global mission planning are solved simultaneously using an integrated solution. This consists of a geometric clustering algorithm which prioritizes the minimization of overall mission time, and an off-policy, model-free Temporal Difference Learning global agent capable of learning about an initially unknown mission environment through simulations. The latter component makes the solution adaptive to missions with different requirements.
Open Access
Advanced air mobility operation and infrastructure for sustainable connected eVTOL vehicle
(MDPI, 2023-05-16) Al-Rubaye, Saba; Tsourdos, Antonios; Namuduri, Kamesh
Advanced air mobility (AAM) is an emerging sector in aviation aiming to offer secure, efficient, and eco-friendly transportation utilizing electric vertical takeoff and landing (eVTOL) aircraft. These vehicles are designed for short-haul flights, transporting passengers and cargo between urban centers, suburbs, and remote areas. As the number of flights is expected to rise significantly in congested metropolitan areas, there is a need for a digital ecosystem to support the AAM platform. This ecosystem requires seamless integration of air traffic management systems, ground control systems, and communication networks, enabling effective communication between AAM vehicles and ground systems to ensure safe and efficient operations. Consequently, the aviation industry is seeking to develop a new aerospace framework that promotes shared aerospace practices, ensuring the safety, sustainability, and efficiency of air traffic operations. However, the lack of adequate wireless coverage in congested cities and disconnected rural communities poses challenges for large-scale AAM deployments. In the immediate recovery phase, incorporating AAM with new air-to-ground connectivity presents difficulties such as overwhelming the terrestrial network with data requests, maintaining link reliability, and managing handover occurrences. Furthermore, managing eVTOL traffic in urban areas with congested airspace necessitates high levels of connectivity to support air routing information for eVTOL vehicles. This paper introduces a novel concept addressing future flight challenges and proposes a framework for integrating operations, infrastructure, connectivity, and ecosystems in future air mobility. Specifically, it includes a performance analysis to illustrate the impact of extensive AAM vehicle mobility on ground base station network infrastructure in urban environments. This work aims to pave the way for future air mobility by introducing a new vision for backbone infrastructure that supports safe and sustainable aviation through advanced communication technology.
Open Access
Advanced mobility flight dynamics restriction to support high availability communication systems
(IEEE, 2024-09-29) Alhashmi, Fatima; Al-Rubaye, Saba; Tsourdos, Antonios
Electric Vertical Take-Off and Landing (eVTOL) platforms play a crucial role in Advanced Air Mobility (AAM) initiatives, particularly in urban environments. Ensuring the safety and reliability of communication networks during air traffic operations is paramount, with communication performance heavily reliant on antenna radiation characteristics. Maintaining consistent communication throughout the entire flight is essential for flight success. However, dynamic maneuvers such as banking turns can result in airframe shadowing, where the vehicle's structure obstructs antenna signals, posing a challenge to communication reliability. This paper proposes a model integrated into eVTOL avionics to mitigate airframe shadowing issues and maintain optimal communication availability during normal flight operations. A new algorithm is proposed, and simulation studies analysis are conducted to assess the impact of airframe shadowing on eVTOL communication performance. Additionally, insights are provided to air traffic management (ATM) and pilots regarding optimal look angles to minimize or avoid airframe shadowing effects.
Open Access
An advanced path planning and UAV relay system: enhancing connectivity in rural environments
(MDPI, 2024-03-06) El Debeiki, Mostafa; Al-Rubaye, Saba; Perrusquía, Adolfo; Conrad, Christopher; Flores Campos, Juan Alejandro
The use of unmanned aerial vehicles (UAVs) is increasing in transportation applications due to their high versatility and maneuverability in complex environments. Search and rescue is one of the most challenging applications of UAVs due to the non-homogeneous nature of the environmental and communication landscapes. In particular, mountainous areas pose difficulties due to the loss of connectivity caused by large valleys and the volumes of hazardous weather. In this paper, the connectivity issue in mountainous areas is addressed using a path planning algorithm for UAV relay. The approach is based on two main phases: (1) the detection of areas of interest where the connectivity signal is poor, and (2) an energy-aware and resilient path planning algorithm that maximizes the coverage links. The approach uses a viewshed analysis to identify areas of visibility between the areas of interest and the cell-towers. This allows the construction of a blockage map that prevents the UAV from passing through areas with no coverage, whilst maximizing the coverage area under energy constraints and hazardous weather. The proposed approach is validated under open-access datasets of mountainous zones, and the obtained results confirm the benefits of the proposed approach for communication networks in remote and challenging environments.
Open Access
AI-driven blind signature classification for IoT connectivity: a deep learning approach
(IEEE, 2022-01-31) Pan, Jianxiong; Ye, Neng; Yu, Hanxiao; Hong, Tao; Al-Rubaye, Saba; Mumtaz, Shahid; Al-Dulaimi, Anwer; Chih-Lin, I.
Non-orthogonal multiple access (NOMA) promises to fulfill the fast-growing connectivities in future Internet of Things (IoT) using abundant multiple-access signatures. While explicitly notifying the utilized NOMA signatures causes large signaling cost, blind signature classification naturally becomes a low-cost option. To accomplish signature classification for NOMA, we study both likelihood- and feature-based methods. A likelihood-based method is firstly proposed and showed to be optimal in the asymptotic limit of the observations, despite high computational complexity. While feature-based classification methods promise low complexity, efficient features are non-trivial to be manually designed. To this end, we resort to artificial intelligence (AI) for deep learning-based automatic feature extraction. Specifically, our proposed deep neural network for signature classification, namely DeepClassifier, establishes on the insights gained from the likelihood-based method, which contains two stages to respectively deal with a single observation and aggregate the classification results of an observation sequence. The first stage utilizes an iterative structure where each layer employs a memory-extended network to explicitly exploit the knowledge of signature pool. The second stage incorporates the straight-through channels within a deep recurrent structure to avoid information loss of previous observations. Experiments show that DeepClassifier approaches the optimal likelihood-based method with a reduction of 90% complexity.
Open Access
AI-enabled interference mitigation for autonomous aerial vehicles in urban 5G networks
(MDPI, 2023-10-13) Warrier, Anirudh; Al-Rubaye, Saba; Inalhan, Gokhan; Tsourdos, Antonios
Integrating autonomous unmanned aerial vehicles (UAVs) with fifth-generation (5G) networks presents a significant challenge due to network interference. UAVs’ high altitude and propagation conditions increase vulnerability to interference from neighbouring 5G base stations (gNBs) in the downlink direction. This paper proposes a novel deep reinforcement learning algorithm, powered by AI, to address interference through power control. By formulating and solving a signal-to-interference-and-noise ratio (SINR) optimization problem using the deep Q-learning (DQL) algorithm, interference is effectively mitigated, and link performance is improved. Performance comparison with existing interference mitigation schemes, such as fixed power allocation (FPA), tabular Q-learning, particle swarm optimization, and game theory demonstrates the superiority of the DQL algorithm, where it outperforms the next best method by 41.66% and converges to an optimal solution faster. It is also observed that, at higher speeds, the UAV sees only a 10.52% decrease in performance, which means the algorithm is able to perform effectively at high speeds. The proposed solution effectively integrates UAVs with 5G networks, mitigates interference, and enhances link performance, offering a significant advancement in this field.
Open Access
Aircraft to operations communication analysis and architecture for the future aviation environment
(IEEE, 2021-11-15) Whitworth, Huw; Al-Rubaye, Saba; Tsourdos, Antonios; Jiggins, Julia; Silverthorn, Nigel; Thomas, Karim
Fifth Generation (5G) systems are envisaged to support a wide range of applications scenarios with varying requirements. 5G architecture includes network slicing abilities which facilitate the partitioning of a single network infrastructure on to multiple logical networks, each tailored to a given use case, providing appropriate isolation and Quality of Service (QoS) characteristics. Radio Access Network (RAN) slicing is key to ensuring appropriate QoS over multiple domains; achieved via the configuration of multiple RAN behaviours over a common pool of radio resources. This Paper proposes a novel solution for efficient resource allocation and assignment among a variety of heterogeneous services, to utilize the resources while ensuring maximum QoS for network services. First, this paper evaluates the effectiveness of different wireless data bearers. Secondly, the paper proposes a novel dynamic resource allocation algorithm for RAN slicing within 5G New Radio (NR) networks utilising cooperative game theory combined with priority-based bargaining. The impact of this work to industry is to provide a new technique for resource allocation that utilizes cooperative bargaining to ensure all network services achieve minimum QoS requirements – while using application priority to reduce data transfer time for key services to facilitate decreased turnaround time at the gate.
Open Access
Airport connectivity optimization for 5G ultra-dense networks
(IEEE, 2020-06-08) Al-Rubaye, Saba; Tsourdos, Antonios
The rapid increase of air traffic demand and complexity of radio access network motivate developing scalable wireless communications by adopting system intelligence. The lack of adaptive reconfiguration in radio transmission systems may cause dramatic impacts on the traffic management concerning congestion and demand-capacity imbalances driving the industry to jointly access licensed and unlicensed bands for improved airport connectivity. Therefore, intelligent system is embedded into fifth generation (5G) ultra-dense networks (UDNs) to provision dense and irregular deployments that maintain extended coverage and also to improve the energy-efficiency for the entire airport network providing high speed services. To define the technical aspects of this solution, this paper addresses new intelligent technique that configures the coverage and capacity factors of radio access network considering the changes in air traffic demands. This technique is analysed through mathematical models that employ power consumption constraints to support dynamic traffic control requirements to improve the overall network capacity. The presented problem is formulated and exactly solved for medium or large airport air transportation network. The power optimization problem is solved using linear programming with careful consideration to latency and energy efficiency factors. Specifically, an intelligent pilot power method is adopted to maintain the connectivity throughout multi-interface technologies by assuming minimum power requirements. Numerical and system-level analysis are conducted to validate the performance of the proposed schemes for both licenced macrocell Long-Term Evolution (LTE) and unlicensed wireless fidelity (WiFi) topologies. Finally, the insights of problem modelling with intelligent techniques provide significant advantages at reasonable complexity and brings the great opportunity to improve the airport network capacity
Open Access
Analysis of synchronization in distributed avionics systems based on time-triggered ethernet
(IEEE, 2021-11-15) Tariq, Nahman; Petrunin, Ivan; Al-Rubaye, Saba
Significant developments are made in unmanned aerial vehicles (UAVs) and in avionics, where messages sent in the network with critical Time play a vital role. Several studies in Time-Triggered Ethernet have been carried out, but these studies. Still, these improving QoS such as latency in end-to-end delays of an internally synchronized TTE network. However, no one monitors integrated modular avionics. We proposed a framework that enables TTE to be externally synchronized from a GNSS to overcome this problem. We have incorporated our proposed Algorithm in the TTE protocol based on specific parameters and multiple existing algorithms. The proposed Algorithm gives us the ability to control and synchronize the TTE network. Also, we have a developed scenario for analyzing the performance of externally synchronized end-to-end latency of TT messages in a TTE network. We simulated scenarios in our framework and analyzed QoS but, more specifically, the latency that affects the performance of time-triggered messages in externally synchronized TTE networks. The result shows that our proposed framework outperforms existing approaches.
Open Access
Attack-detection architectural framework based on anomalous patterns of system performance and resource utilization - Part II
(IEEE, 2021-06-11) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios; Shaw, Carl
This paper presents a unique security approach for detecting cyber-attacks against embedded systems (ESs). The proposed approach has been shaped within an architectural framework called anomalous resource consumption detection (ARCD). The approach’s detection mechanism detects cyber-attacks by distinguishing anomalous performance and resource consumption patterns from a pre-determinable reference model. The defense mechanism of this approach acts as an additional layer of protection for ESs. This technique’s effectiveness was previously evaluated statistically, and in this paper, we tested this approach’s efficiency computationally by using the support-vector machine algorithm. The datasets were generated and collected based on a testbed model, where it was run repeatedly under different operation conditions (normal cases (Rs) versus attacked cases). The executed attack scenarios are 1) denial-of-service (DoS); 2) brute force (BF); and 3) remote code execution (RCE), and man-in-the-middle (MITM). A septenary tuple model, which consists of seven determinants that are analyzed based on seven statistical criteria, is the core of the detection mechanism. The prediction accuracy in terms of classifying anomalous patterns compared to normal patterns based on the confusion matrix revealed promising results, proving this approach’s effectiveness, where the final results confirmed very high prediction accuracies in terms of distinguishing anomalous patterns from the typical patterns. Integrating the ARCD concept into an operating system’s functionality could help software developers augment the existing security countermeasures of ESs. Adopting the ARCD approach will pave the way for software engineers to build more secure operating systems in line with the embedded system’s capabilities, without depleting its resources.
Open Access
Blockchain-based secure and intelligent data dissemination framework for UAVs in battlefield applications
(IEEE, 2023-09-30) Jadav, Nilesh Kumar; Rathod, Tejal; Gupta, Rajesh; Tanwar, Sudeep; Kumar, Neeraj; Iqbal, Rahat; Atalla, Shadi; Mohammad, Hijji; Al-Rubaye, Saba
The modern warfare scenario has immense challenges that can risk personnel's lives, highlighting the need for data acquisition to win a military operation successfully. In this context, unmanned aerial vehicles (UAVs) play a significant role by covertly acquiring reconnaissance data from an enemy location to make the friendly troops aware. The acquired data is mission-critical and needs to be secured from the intruders, which can implicitly manipulate it for their benefit. Moreover, UAVs collect a large amount of data, including high-definition images and surveillance videos; handling such a massive amount of data is a bottleneck on traditional communication networks. To mitigate these issues, this article proposes a blockchain and machine learning (ML)-based secure and intelligent UAV communication underlying sixth-generation (6G) networks, that is, Block-USB. The proposed system refrain the disclosure of highly-sensitive military operations from intruders (either a rogue UAV or a malicious controller). The proposed system uses off-chain storage, that is, Interplanetary file system (IPFS), to improve the blockchain storage capacity. We also present a case study on securing UAV-based military operations by considering multiple scenarios considering controller/UAV malicious. The performance of the proposed system outperforms the traditional baseline 4G/5G and non IPFS-based systems in terms of classification accuracy, communication latency, and data scalability.
Open Access
Communication network architecture with 6G capabilities for urban air mobility
(IEEE, 2024-02-28) Al-Rubaye, Saba; Conrad, Christopher; Tsourdos, Antonios
As the demand for urban air mobility (UAM) increases, a robust communication, navigation, and surveillance (CNS) network architecture is needed to support the integration of sustainable UAM vehicles and technologies. Specifically, a new digital communication infrastructure is imperative to support increased levels of digitisation and autonomy within the aviation industry. This infrastructure must remain compatible with existing technologies, while enabling the integration of future 6G systems. This paper thereby discusses the communication challenges and opportunities associated with UAM integration. Potential communication technologies and standards needed to support UAM operations are presented and consolidated into a unified communication architecture with ground-, air-, and satellite-based infrastructure. The functional requirements of this architecture are also discussed, to enable seamless communication between UAM vehicles, air traffic control, and other ground- or air-based systems. Notably, 6G is highlighted as a key enabler of dense and sustainable UAM operations with high data traffic demands. A simple link budget analysis for a 6G air-to-ground data link in a green urban environment is thereby performed, em-phasising the infrastructural development necessary to support 6G roll-out. These findings pave the way for a more sustainable and accessible UAM transportation system, backed by a secure and reliable communication infrastructure.
Open Access
Cybersecurity of embedded systems a novel approach for detecting cyberattacks based on anomalous patterns of resource utilisation
(Cranfield University, 2022-01) Aloseel, Abdulmohsan; Al-Rubaye, Saba; Zolotas, Argyrios
An embedded system (ES) is a processing unit that has been embedded into a larger cyber-physical system (CPS) to steer its functions. The ES has played an essential role in modern life, where it has been used widely in sensing, controlling and computing for countless applications in different domains, such as the internet of things (IoT), smart cities, healthcare, transportation, communication, military, transportation, gas distribution, avionics and national infrastructures. Due to its widespread application in different domains and its evolution in conjunction with many key technologies, it is crucial that these systems are secured against cyberattacks as the ES has the same generic security goals – confidentiality, integrity and availability – as conventional computer systems. Although the ES is exposed to the numerous and unpredicted security threats that are experienced by conventional computer systems, it is significantly limited in its ability to manage the advanced security solutions that are implemented on conventional computer systems. The limitations in resources of the ES, due to its identity or characteristics, impose tight constraints on both its communication and computing capacity, thereby hindering the implementation of advanced security solutions. Thus, the cybersecurity of an ES is limited by constraints on its resources rather than by the absence of advanced security solutions. There is an urgent need, therefore, to develop security solutions that are compatible with the capabilities of the ES. This study tried to bridge the gap by addressing both theoretical and empirical aspects of ES cybersecurity. The study can be divided into three main blocks. The first block identifies the key factors, involved parties or entities, and creates the cybersecurity landscape for embedded systems (CSES), while considering the conflict between the requirements for cybersecurity and the computing capabilities of an ESs. Additionally, twelve factors influencing CSES have been extracted and identified based on the direction of the research. These factors have been used to shape a multiple layers feedback framework of embedded system cybersecurity (MuLFESC), with nine layers of protection. It has been developed in line with an expanded model of risk assessment metrics, which will enable cybersecurity practitioners to evaluate the security countermeasures of their systems and assist in the development of more comprehensive solutions for CSES. A novel security approach, called anomalous resource consumption detection (ARCD), was developed in the second block of this study. This involved the design of a testbed to provide a realistic hardware-software environment to analyse an example application of an ES. A Smart PiCar was run repeatedly under different operational conditions – typical conditions and under attack. The data of seven designated parameters based on seven statistical criteria was analysed to measure the range, pattern of performance and resource utilisation. The results from this statistical analysis demonstrated the potential for defining a standard pattern for the resource utilisation and performance of the embedded system due to a significant similarity with the values of the parameters at normal states. In contrast, the results from the attacked cases showed a definite and detectable impact on the consumption and performance of the resources of the ES, which presented anomalous patterns. The ARCD method can be implemented as an additional layer of protection to detect cyber-attacks in an ES, where a septenary tuple model, consisting of seven parameters, is the core of the detection mechanism. In the final block, the ARCD approach has been placed within an architectural framework, which may pave the way for software engineers to build secure operating systems in line with the capabilities of the ES. The architectural framework was developed after the efficiency of the approach was computationally validated by machine learning. This involved the design of a classifier and predictor model to find the predictive accuracy percentage in terms of separating patterns of anomalous performance and resource utilisation from the typical pattern. Based on the confusion matrix, the prediction accuracy for classifying anomalous patterns compared with default patterns revealed promising results, thus proving the effectiveness of the ARCD approach. The results confirmed very high prediction accuracies as regards distinguishing anomalous patterns from the typical patterns.
Open Access
Deep learning based secure transmissions for the UAV-RIS assisted networks: trajectory and phase shift optimization
(IEEE, 2024-12-08) Li, Jiawei; Wang, Dawei; Zhang, Jiankang; Alfarraj, Osama; He, Yixin; Al-Rubaye, Saba; Yu, Keping; Mumtaz, Shahid
This paper investigates the secure transmissions in the Unmanned Aerial Vehicle (UAV) communication network facilitated by a Reconfigurable Intelligent Surface (RIS). In this network, the RIS acts as a relay, forwarding sensitive information to the legitimate receiver while preventing eavesdropping. We optimize the positions of the UAV at different time slots, which gives another degree to protect the privacy information. For the proposed network, a secrecy rate maximization problem is formulated. The non-convex problem is solved by optimizing the RIS's phase shifts and UAV trajectory. The RIS phase shift optimization problem is converted into a series of subproblems, and a non-linear fractional programming approach is conceived to solve it. Furthermore, the first-order taylor expansion is employed to transform the UAV trajectory optimization into convex function, and then we use the deep Q-network (DQN) method to obtain the UAV's trajectory. Simulation results show that the proposed scheme enhances the secrecy rate by 18.7% compared with the existing approaches.
Open Access
Developing secure hardware for UAV authorisation using lightweight authentication
(IEEE, 2023-11-10) Sen, Muhammet A.; Al-Rubaye, Saba; Tsourdos, Antonios
In this research, a unique authorisation strategy for unmanned aerial vehicle (UAV) systems is presented. UAVs are being used more frequently in a variety of military and civilian missions, including observation, reconnaissance, transportation, search and rescue, traffic surveillance, weather forecasting, and logistics operations. To address the need for secure authorization in UAV networks, we propose an innovative scheme that leverages Physically Unclonable Functions (PUFs) within the Flying Ad-Hoc Network (FANET) structure. Our plan strengthens the security and integrity of UAV operations by incorporating PUF technology. The suggested authorization architecture is appropriate for the dynamic and resource-constrained FANET context because it ensures effective and trustworthy authentication while minimising computational cost. Through this study, we hope to further the creation of reliable permission systems for UAV systems used in both military and civilian applications.
Open Access
Differentially-private federated intrusion detection via knowledge distillation in third-party IoT systems of smart airports
(IEEE, 2023-10-24) Chen, Yang; Al-Rubaye, Saba; Tsourdos, Antonios; Baker, Lawrence; Gillingham, Colin
With the increasing deployment of IoT and Industry 4.0, the federated learning system was presented to preserve the privacy between the third-party IoT systems and the security operation center in smart airports. Nonetheless, the extremely skewed distribution of cyber threats increases the complexity of intrusion detection system (IDS) in smart airports, while privacy preservation limits the utility of IDS in the process of server model update. In this article, we have devised a knowledge distillation (KD)-based Convolutional Neural Network and Gated Recurrent Unit (CNN-GRU) model to improve the accuracy of multiple intrusion detection. In addition, the tradeoff between privacy and accuracy is achieved by denoising the adaptive parameter update mechanism to upgrade the optimizer of Differentially-Private (DP) Federated IDS. The results indicate high effectiveness and robustness of DP Federated KD-based IDS for third-party IoT systems of a smart airport.
Open Access
Digital twin analysis to promote safety and security in autonomous vehicles
(IEEE, 2021-03-1) Almeaibed, Sadeq; Al-Rubaye, Saba; Tsourdos, Antonios; Avdelidis, Nicolas Peter
With the new industrial revolution of digital transformation, more intelligence and autonomous systems can be adopted in the manufacturing transportation processes. Safety and security of autonomous vehicles (AVs) have obvious advantages of reducing accidents and maintaining a cautious environment for drivers and pedestrians. Therefore, the transformation to data-driven vehicles is associated with the concept of digital twin, especially within the context of AV design. This also raises the need to adopt new safety designs to increase the resiliency and security of the whole AV system. To enable secure autonomous systems for smart manufacturing transportation in an end-to-end fashion, this article presents the main challenges and solutions considering safety and security functions. This article aims to identify a standard framework for vehicular digital twins that facilitate the data collection, data processing, and analytics phases. To demonstrate the effectiveness of the proposed approach, a case study for a vehicle follower model is analyzed when radar sensor measurements are manipulated in an attempt to cause a collision. Perceptive findings of this article can pave the way for future research aspects related to employing digital twins in the AV industry.