Unmanned aerial vehicles (UAVs) are becoming increasingly prevalent in modern society, but are often insecure by design or limited in security capability due to computational constraints. The growing open-source UAV ecosystem has further enabled custom software and hardware development, frequently without adherence to established best practices. At the centre of this ecosystem is the MAVLink protocol, used primarily, but not exclusively, for communication between UAVs and their associated Ground Control Stations (GCSs). Since the adoption of MAVLink 2.0 in 2017, limited research has been conducted on its in-built security mechanisms. Although MAVLink 2.0 supports message signing, its signature scheme remains highly vulnerable. This study presents a novel attack that exploits GPS-based timestamp manipulation without the knowledge of a UAV’s secret key. The attack is evaluated in simulation, hardware-in-the-loop tests, and hardware environment tests. This paper also outlines potential countermeasures and briefly discusses the broader applicability of the attack to other GPS-synchronised systems beyond UAVs.

The convergence of autonomous aerial systems and networking technologies has given rise to the Internet of Drones (IoD) as a compelling paradigm, gaining significant attention from academia and industry stakeholders. Drones often operate in swarm formations to collaboratively achieve autonomous coordination and aerial intelligence, thereby forming a Flying Ad Hoc Network (FANET). However, the persistent vulnerability remains in the insecure communication link, exposing the network to eavesdropping and unauthorized access. Addressing such shortcomings necessitates a robust Authentication and Key Exchange (AKE) protocol. Therefore, we have designed a quantum secure AKE protocol integrating NIST-proven quantum secure primitives, including ML-DSA, symmetric AES, and hash functions. To the best of our knowledge, this is the first AKE protocol that leverages a quantum secure signature scheme for securing IoD-enabled FANET applications. The designed protocol incorporates hardware-specific fingerprinting integrated with a noise tolerance mechanism to eliminate the risk of unauthorized device tampering. The use of re-synchronization and robust security measures for credential management further enhances its resilience against desynchronization and stolen attacks. The findings of performance evaluation exhibit the superiority of the designed protocol over the prevalent AKE protocols, with a remarkable reduction of 67.62% in computation cost while achieving a 50% improvement in overall security. Finally, implementing a complete authentication cycle using PIX32 and Pixhawk 6C drones sets a new benchmark as a practical validation of the designed AKE protocol within a real-world IoD testbed.

The authentication and key establishment (AKE) mechanism is considered one of the promising solutions for securing communication in Internet of Drones (IoD) applications. Nevertheless, existing AKE mechanisms based on traditional cryptographic techniques rely on integer factorization and discrete logarithms, which are no longer safe with the advent of quantum computers. These shortcomings motivate us to design a cutting-edge Quantum Secure Authentication and Key-Establishment mechanism (QSAKE) for the IoD environment. To the best of our knowledge, QSAKE is the pioneered work that uses advanced quantum-safe cryptography, providing a strong defence beyond traditional methods. To further enhance security, it eliminates storing long-term secrets directly in drone memory, reducing the risk of unauthorized access. A Holybro Pixhawk-based microcontroller is used with a Raspberry Pi connected to a Xilinx Arty A7-100T FPGA board to develop a realistic testbed. Finally, this work stands out as a groundbreaking application of a complete authentication process within a practical IoD testbed, demonstrating its high efficacy and practicality.

Intrusion detection in Industrial Control Systems (ICS) remains a significant challenge due to strict real-time constraints and evolving cyber threats. Although deep learning models like autoencoders have shown promise, they often require large datasets and are limited in their ability to generalize across scenarios. In this paper, we propose a novel intrusion detection method based on Variational Quantum Classifiers (VQC). By leveraging quantum circuits for feature encoding and classification, our method demonstrates strong anomaly detection capabilities, even with limited data. We evaluate the VQC model on a subset of the WUSTL-IIOT-2018 dataset and compare its performance to a classical Conditional Variational Autoencoder (CVAE) model. The VQC approach achieves $99.00 \%$ accuracy, perfect recall, and 0.9947 AUC-ROC, showcasing its potential as a lightweight and accurate anomaly detector for resourceconstrained ICS environments. This work provides one of the first explorations of quantum machine learning for ICS security and outlines key directions for future research on real quantum hardware and larger-scale deployments.

With the growing presence of drones in our skies, securing their operations and ensuring reliable communication has become more crucial than ever. These drones form interconnected networks known as the Internet of Drones (IoD) to facilitate real-time coordination, autonomous aerial surveillance, and special aerial tasks. However, the interaction between drones and ground stations occurs over unregulated and dynamic communication channels, introducing security vulnerabilities such as impersonation, Man-in-the-Middle (MitM), and forgery attacks. Implementing robust authentication protocols can serve as a promising solution to protect drone operations and communication, thereby ensuring the safety and security of our skies. In this article, we introduce a novel authentication and key establishment protocol that uniquely integrates level-triggered Physically Unclonable Function (PUF), BCH error-correcting code, and AES-GCM symmetric encryption, setting a new standard for secure and reliable communication between drones and ground stations. We demonstrate the robustness of our protocol through comprehensive security verification using the Scyther tool, coupled with formal validation within the Random Oracle Model (ROM). Through rigorous performance analysis, we demonstrate the superiority of our protocol over baseline protocols, achieving 64.37% greater efficiency in computation and 26.03% reduction in communication overheads. We also present a realistic implementation of our protocol using Pix32 v6 companion with Raspberry Pi as drone and laptop device as ground station server. The PUF has been implemented in Xilinx Arty A7-100T FPGA board. To the best of our knowledge, this is the first work demonstrating the implementation of a complete authentication cycle in real-world IoD settings.

Unmanned Aerial Vehicles (UAVs), commonly known as drones are aircraft without a human pilot, crew, or passenger on board. An Unmanned Aerial Vehicle swarm (UAV swarm) usually consists of three or more drones and can execute complex tasks and missions. UAV and UAV Swarm have proliferated these years and have significantly impacted vari-ous fields, including military, agriculture, disaster relief, rescue supplies, and energy management. However, these advantages require enhanced safety measures to prevent group collisions, secure communication within the swarm, and detect abnormal behaviours, including sensor faults and cyber-attacks (e.g., GPS spoofing and command hijacking). This paper proposes an anomaly detection method based on Bi-LSTM with an attention mechanism to identify abnormal behaviours within a UAV swarm. The proposed method uses a supervised approach, training and validating the model with data collected and labelled from self-designed simulated UAV swarm flight experiments. These swarm flight tests include normal flights, flights in windy environments, flights with simulated cyber-attacks and fault injection. These multiple flights enable the model to learn to detect anomalies during formation missions across different conditions. Experimental results demonstrate that the anomaly detection model effectively processes time series data and learns its features from UAV swarm. The evaluation confirms that this method is a sensible solution for detecting anomalies within UAV swarm flights.

Context awareness has been on the grow for the last couple of years and part of the reason for the emergence of this technology is the nature and dynamic of the Internet of Things (IoT) itself. Context awareness provides an additional layer of security for the ever-changing IoT entourage by providing an aware approach to security decisions. Traditional security mechanisms might not be always suitable for the context of IoT, thus context-aware security mechanism comes out as a suitable solution. Context awareness is a technique for identifying contextual circumstances using a particular context framework model of awareness, followed by identifying an appropriate solution that is centred on the findings. This paper presents a holistic view of context awareness for IoT security. Specifically, we intend to analyze how context awareness contributes to IoT security, we focus on investigating the context-aware security solutions proposed in the literature and exploring the techniques and approaches used to enhance IoT security based on contextual information, their effectiveness and benefits together with impediments of these solutions. In addition, we discuss the challenges associated with using context-aware security in the IoT and future research directions that need to be addressed in order to make contextual information more effective for security solutions in IoT.

Quality of Service (QoS) within the Internet of Things (IoT) by facilitating decentralized processing and bringing the computation and storage resources closer to the network edge. It reduces the latency of application responses to the end-users when compared to the cloud computing only-based approaches. One of the major challenges that hinder the ubiquitous adoption of IoT technologies is the efficient handling of resource allocation. There is a necessity for a smart layer that would facilitate efficient and adaptive scheduling between the edge and fog nodes, especially in light of the dynamic and heterogeneous nature of today's fog/cloud-based IoT systems. In this study, we present a theoretical model for such an intermediary layer middleware, with an objective to enable QoS-aware allocation of fog resources.

Large enterprises offer thousands of micro-services applications to support their daily business activities by using Application Programming Interfaces (APIs). These applications generate huge amounts of traffic via millions of API calls every day, which is difficult to analyze for detecting any potential abnormal behaviour and application outage. This phenomenon makes Machine Learning (ML) a natural choice to leverage and analyze the API traffic and obtain intelligent predictions. This paper proposes an ML-based technique to detect and classify API traffic based on specific features like bandwidth and number of requests per token. We employ a Support Vector Machine (SVM) as a binary classifier to classify the abnormal API traffic using its linear kernel. Due to the scarcity of the API dataset, we created a synthetic dataset inspired by the real-world API dataset. Then we used the Gaussian distribution outlier detection technique to create a training labeled dataset simulating real-world API logs data which we used to train the SVM classifier. Furthermore, to find a trade-off between accuracy and false positives, we aim at finding the optimal value of the error term (C) of the classifier. The proposed anomaly detection method can be used in a plug and play manner, and fits into the existing micro-service architecture with little adjustments in order to provide accurate results in a fast and reliable way. Our results demonstrate that the proposed method achieves an F1-score of 0.964 in detecting anomalies in API traffic with a 7.3% of false positives rate.

5G is poised to support new emerging service types that help in the realization of futuristic applications. These services include enhanced Mobile BroadBand (eMBB), ultra-Reliable Low Latency Communication (uRLLC), and massive Machine-Type Communication (mMTC). Even though the new services offer a variety of new use-cases to be implemented, it is still a challenge to guarantee the Quality of Service (QoS) they demand. Moreover, as considerable amount of computational resources are introduced in the evolved Radio Access Network (RAN) following the Mobile Edge Computing (MEC) concept, computational resource allocation optimization along with radio allocation becomes essential. In this paper, we examine the characteristics of the new 5G services and propose a joint computational and radio resource allocation framework that analyzes the QoS performance of each 5G service individually. The framework is developed based on per-service load characterization. Therefore, a computational load distribution algorithm is developed that balances the workloads subject to user association constraint. Further, radio resource allocation performs load-based eMBB-mMTC slicing and uRLLC puncturing. The simulation results show that the proposed solution reduces the packet loss ratio by up to 15% and increases the user data rate by up to 7% for 4G-like services. Furthermore, the effect of resource granularity in radio allocation has been identified as crucial factor for effective allocation of services with small data loads. Finally, the problem of small granularity has been solved by adapting the allocation interval.

Recently, cloud computing has emerged as a primary enabling technology to provide compute, storage, platform, and analytics services to end-users and organizations based on pay-as-you-use. In essence, cloud provides agility, availability, scalability, and resiliency. However, increased number of users leads to issues such as scheduling of requests, demands, and work-load efficiency over the available cloud resources. Similarly, since the inception of cloud computing, task scheduling is reckoned as an essential ingredient in the commercial value of this technology. Task scheduling is considered as an NP-hard problem in cloud computing and different solutions exist in the literature to address this issue. In this paper, we investigate and empirically compare some of the recent state-of-the-art scheduling mechanisms in cloud computing with respect to Makespan (the time difference between the start and finish of a sequence of jobs or tasks) and throughput (number of tasks successfully executed per unit time (Makespan)). We then extend the comparison by evaluating the considered approaches with respect to Average Resource Utilization Ratio (ARUR). We also recommend and identify factors that can improve resource utilization and maximize revenue-generation for cloud service providers.

Location-based encryption enhances security through integration of location data which is based on Global Positioning System (GPS) coordinates into encryption and decryption processes. It allows data to be decrypted only at specific location(s) or within a specific area. However, this approach strictly relies on self-checked location data which can be easily bypassed. In this paper, we present an analysis of the security of the existing location-based key exchange methods together with our proposed improvements. Furthermore, we propose a novel method based on the existence of a Trusted Third Party (TTP). A TTP is an entity trusted by both sides and the location tracking is entrusted to a TTP rather than a client. We also demonstrate a working proof-of-concept for the proposed approach.

The past few years have witnessed unprecedented advancements in the Distributed Ledger Technology (DLT) and blockchain - a form of DLT. DLT has clearly expanded the applications landscape in various sectors of our lives ranging from banking to business, finance, industry, education, and so on. On the other hand, security plays a crucial part in the successful realization of such applications and services. To this end, cryptography is the primary mean to protect the applications, networks, infrastructure, and services from cyber-threats. However, the existing Public Key Infrastructure (PKI) is based on central Certificate Authority (CA) that can become a bottleneck and may affect the efficiency of the cryptographic protocols because of the overhead incurred by the verification of cryptographic signatures and certificates. Recently, blockchain has also been leveraged to aid PKI without the need for a central authority. In this spirit, in this paper, we develop and implement a blockchain-based PKI using open-source Hyperledger Sawtooth. The proposed blockchain-based approach helps to address the problems of the existing PKI such as compromised and misbehaving CAs.

Development of Distributed Ledger Technology (DLT)-based applications requires an appropriate platform that meets the application requirements. However, due to the abundance of such platforms such as Ethereum, NEM, IOTA, and OpenChain, and the differences among them in terms of scalability, throughput, and features, it is not easy to select a platform for a given use-case. Selection of the right DLT platform is pivotal for the performance of applications and thus-forth directly affects consumer satisfaction. Therefore, the aforementioned factors must be taken into account to decide on a particular platform. To fill this gap, in this paper, we conduct a comparative analysis of different DLT platforms. The choice of platform is based on their popularity and current market share as well as the evolving trends and approaches. In essence, we choose Ethereum, EOS, Hyperledger Sawtooth and NEO. We compare these platforms from both development and performance perspectives. The comparison revealed that Sawtooth provides a huge customization capability that affects the performance and EOS maintains a stable throughput under varying network scales and loads.

Internet of Things (IoT) can be seen as a pervasive network of networks: numerous heterogeneous entities both physical and virtual interconnected with any other entity or entities through unique addressing schemes, interacting with each other to provide/request all kinds of services. IoT technology is expected to pave the way for groundbreaking applications in a diversity of areas such as healthcare, security and surveillance, transportation, and industry, and integrate advanced technologies of communication, networking, cloud computing, sensing and actuation. Given the enormous number of connected devices that are potentially vulnerable, highly significant risks emerge around the issues of security, privacy, and governance; calling into question the whole future of IoT. IoT applications are expected to affect many aspects of people's lives, bringing about many conveniences; however, if security and privacy cannot be ensured, this can lead to a number of undesired consequences. This survey focuses on the security aspects of IoT, and discusses up-to-date IoT security solutions.