WiFi-based indoor localization is essential for asset tracking, healthcare monitoring, and smart buildings. However, existing systems face challenges such as RSS variability, environmental noise, and difficulty in detecting floor and building levels, compounded by limited labeled data and the high costs of collecting received signal strength (RSS). This paper introduces quantum stochastic contrastive learning (QSCL), a novel framework grounded in rigorous theoretical foundations. We present four theorems and one lemma that establish bounded probabilistic augmentation, diversity of the strong view, the suitability of the symmetric contrastive objective under heterogeneous augmentation channels, and expected similarity stability under zero-mean perturbations, supported by formal proofs. Leveraging these foundations, QSCL uses quantum computing (QC) to generate strong data augmentations via stochastic perturbations, thereby enhancing data diversity, while classical weak augmentations provide subtle variations for robust feature learning. We propose a spatio-temporal encoder (STE) that integrates convolutional layers with channel and spatial attention modules (CBAM-style) to capture spatial and temporal dependencies in sequential data. Furthermore, a symmetric cross-view contrastive loss is introduced to capture forward and reverse relationships between augmented views, ensuring robust representations. Comprehensive evaluations on the UJIIndoorLoc and UTSIndoorLoc datasets validate QSCL, demonstrating superior performance with limited labeled data and resilience to quantum and measurement noise. The proposed framework significantly improves localization accuracy, floor and building detection, and generalizability in challenging indoor environments.

Modern supply chains must balance high-speed logistics with environmental impact and security constraints, prompting a surge of interest in AI-enabled Internet of Things (AIoT) solutions for global commerce. However, conventional supply chain optimization models often overlook crucial sustainability goals and cyber vulnerabilities, leaving systems susceptible to both ecological harm and malicious attacks.To tackle these challenges simultaneously, this work integrates a quantum-inspired reinforcement learning framework that unifies carbon footprint reduction, inventory management, and cryptographic-like security measures. We design a quantum-inspired reinforcement learning framework that couples a controllable spin-chain analogy with real-time AIoT signals and optimizes a multi-objective reward unifying fidelity, security, and carbon costs. The approach learns robust policies with stabilized training via value-based and ensemble updates, supported by window-normalized reward components to ensure commensurate scaling. In simulation, the method exhibits smooth convergence, strong late-episode performance, and graceful degradation under representative noise channels, outperforming standard learned and model-based references, highlighting its robust handling of real-time sustainability and risk demands. These findings reinforce the potential for quantum-inspired AIoT frameworks to drive secure, eco-conscious supply chain operations at scale, laying the groundwork for globally connected infrastructures that responsibly meet both consumer and environmental needs.

Sentiment analysis (SA) is an essential component of natural language processing (NLP) and is used to analyze sentiments, attitudes, and emotional tones in various contexts. It provides valuable insights into public opinion, customer feedback, and user experiences. Researchers have developed various classical machine learning (ML) and neuro-fuzzy approaches to address the exponential growth of data and the complexity of language structures in SA. However, these approaches often fail to determine the optimal number of clusters, interpret results accurately, handle noise or outliers efficiently, and scale effectively to high-dimensional data. In addition, they are frequently insensitive to input variations. In this article, we propose a novel hybrid approach for SA called the quantum fuzzy neural network (QFNN), which leverages quantum properties and incorporates a fuzzy layer to overcome the limitations of classical SA algorithms. In this study, we test the proposed approach on two Twitter datasets: the Coronavirus Tweets Dataset (CVTD) and the General Sentimental Tweets Dataset (GSTD), and compare it with classical and hybrid algorithms. The results show that QFNN outperforms all classical, quantum, and hybrid algorithms, achieving 100% and 90% accuracy in the case of CVTD and GSTD, respectively. Furthermore, QFNN demonstrates its robustness against six different noise models, providing the potential to tackle the computational complexity associated with SA on a large scale in a noisy environment. The proposed approach expedites sentiment data processing and precisely analyzes different forms of textual data, thus improving sentiment classification and insights associated with SA.

This study presents, to our knowledge, the first comprehensive evaluation of seven Machine Learning (ML)based one-shot object tracking algorithms for the vision-based position stabilization of remotely-operated underwater vehicles (ROVs). We introduce a position-locking framework that analyzes images of a target object, in front of which the ROV must maintain stability. The system leverages the outputs of various object-tracking algorithms to autonomously adjust the ROV’s position in response to external disturbances. Extensive realworld experiments were conducted using a BlueROV2 platform in an indoor pool, highlighting the advantages and limitations of each tracking method. Additionally, to address the lack of publicly available underwater ROV datasets, we are releasing our collected data as open-source, aiming to support and advance future research in this field.

Satellite communication is expected to play a key role in future networks due to its ability to deliver wide-area coverage and high-capacity links. Inter-satellite communication (ISC), which facilitates real-time data exchange between satellites, is therefore critical for important satellite applications such as navigation, earth observation, and defense. However, the broadcast nature of the wireless medium renders ISC vulnerable to various security threats. In this paper, we investigate impersonation attack scenarios in LEO ISC and propose a novel machine learning (ML)-based physical layer authentication (PLA) scheme. The proposed method leverages Doppler frequency shift (DFS) features, arising from relative satellite motion, to enable secure authentication of the transmitting satellite. To address the challenge of acquiring ground-truth labels, we employ a long short-term memory (LSTM) network to learn temporal patterns from the satellite dynamics. A synthetic dataset simulating a 30-day mission involving three satellites (two legitimate and one malicious) is generated using a MATLAB-based orbital propagation method, incorporating 3D position and velocity vectors. The LSTM model is trained on 25 days of data from legitimate satellites and evaluated over the remaining 5 days using both legitimate and malicious transmissions. Authentication is performed via binary hypothesis testing, and we derive tractable analytical expressions for the false alarm and missed detection probabilities and validate the results through simulations.

Abstract Satellite communications have emerged as a promising extension of terrestrial networks in future 6G network research due to their extensive coverage in remote areas and their ability to support the increasing traffic rate and heterogeneous networks. Like other wireless communication technologies, satellite signals are transmitted in a shared medium, making them vulnerable to attacks such as eavesdropping, jamming, and spoofing. A good candidate to overcome these issues is physical layer security (PLS), which utilizes physical layer characteristics to provide security, mainly due to its suitability for resource‐limited devices such as satellites and IoT devices. This paper provides a comprehensive and up‐to‐date review of PLS solutions to secure satellite communication. Main satellite applications are classified into five domains: satellite‐terrestrial, satellite‐based IoT, satellite navigation systems, FSO‐based, and inter‐satellite. In each domain, how PLS can improve the overall security of the system, preserve desirable security properties, and resist widespread attacks are discussed and investigated. Finally, some gaps in the related literature are highlight and open research problems, including uplink secrecy techniques, smart threat models, authentication and integrity techniques, PLS for inter‐satellite links, and machine learning‐based PLS, are discussed.

In recent years, quantum computing has made significant contributions to many emerging technologies. However, it also poses serious security challenges to these technologies, and one of them is Healthcare Internet of Things (HC-IoT) applications. The devices used in HC-IoT often have limited power, memory, and computational resources, making them especially vulnerable to various cyberattacks. Even a small security breach could cause serious problems, from general system failures to risks that directly affect patients’ diagnoses and treatment. To address this important issue, we review research from 2017 to 2025, examining both the strengths and weaknesses of the technology across various subdomains of the healthcare system. We begin by presenting a taxonomy of healthcare, along with a breakdown of different domains where this technology has been applied or holds potential for future use. This foundation helps establish the motivation and context for the study. Next, we discuss various security threats, considering both the pre-quantum and post-quantum computing eras. Then, we explore existing studies to see what progress has been made and what is still needed. Finally, we point out key security challenges that need more attention from the research community. Lastly, we provide a comparative analysis with existing review articles to address the question of why this article is needed in the presence of published reviews.

Low-energy carbon Internet of Things (IoT) systems are essential for sustainable development, as they reduce carbon emissions while ensuring efficient device performance. Although classical algorithms manage energy efficiency and data processing within these systems, they often face scalability and real-time processing limitations. Quantum algorithms offer a solution to these challenges by delivering faster computations and improved optimization, thereby enhancing both the performance and sustainability of low-energy carbon IoT systems. Therefore, we introduced three quantum algorithms: quantum neural networks utilizing Pennylane (QNN-P), Qiskit (QNN-Q), and hybrid quantum neural networks (QNN-H). These algorithms are applied to two low-energy carbon IoT datasets—room occupancy detection (RODD) and GPS tracker (GPSD). For the RODD dataset, QNN-P achieved the highest accuracy at 0.95, followed by QNN-H at 0.91 and QNN-Q at 0.80. Similarly, for the GPSD dataset, QNN-P attained an accuracy of 0.94, QNN-H 0.87, and QNN-Q 0.74. Furthermore, the robustness of these models is verified against six noise models. The proposed quantum algorithms demonstrate superior computational efficiency and scalability in noisy environments, making them highly suitable for future low-energy carbon IoT systems. These advancements pave the way for more sustainable and efficient IoT infrastructures, significantly minimizing energy consumption while maintaining optimal device performance.

Energy-efficient healthcare systems are becoming increasingly critical for Industry 5.0 as the Internet of Medical Things (IoMT) expands, particularly with the integration of 5G technology. 5G-enabled IoMT systems allow real-time data collection, high-speed communication, and enhanced connectivity between medical devices and healthcare providers. However, these systems face energy consumption and data security challenges, especially with the growing number of connected devices operating in Industry 5.0 environments with limited power resources. Quantum computing integrated with machine learning (ML) algorithms, forming quantum machine learning (QML), offers exponential improvements in computational speed and efficiency through principles such as superposition and entanglement. In this paper, we propose and evaluate three QML algorithms, which are UU, variational UU, and UU-quantum neural networks (QNN) for classifying data from four different datasets: 5G-South Asia, Lumos5G 1.0, WUSTL EHMS 2020, and PS-IoT. Our comparative analysis, using various evaluation metrics, reveals that the UU-QNN method not only outperforms the other algorithms in the 5G-South Asia and WUSTL EHMS 2020 datasets, achieving 100% accuracy, but also aligns with the human-centric goals of Industry 5.0 by allowing more efficient and secure healthcare data processing. Furthermore, the robustness of the proposed quantum algorithms is verified against several noisy channels by analyzing accuracy variations in response to each noise model parameter, which contributes to the resilience aspect of Industry 5.0. These results offer promising quantum solutions for 5G-enabled IoMT healthcare systems by optimizing data classification and reducing power consumption while maintaining high levels of security even in noisy environments.

Social financial technology focuses on trust, sustainability, and social responsibility, which require advanced technologies to address complex financial tasks in the digital era. With the rapid growth in online transactions, automating credit card fraud detection and loan eligibility prediction has become increasingly challenging. Classical machine learning (ML) models have been used to solve these challenges; however, these approaches often encounter scalability, overfitting, and high computational costs due to complexity and high-dimensional financial data. Quantum computing (QC) and quantum machine learning (QML) provide a promising solution to efficiently processing high-dimensional datasets and enabling real-time identification of subtle fraud patterns. However, existing quantum algorithms lack robustness in noisy environments and fail to optimize performance with reduced feature sets. To address these limitations, we propose a quantum feature deep neural network (QFDNN), a novel, resource efficient, and noise-resilient quantum model that optimizes feature representation while requiring fewer qubits and simpler variational circuits. The model is evaluated using credit card fraud detection and loan eligibility prediction datasets, achieving competitive accuracies of 82.2% and 74.4%, respectively, with reduced computational overhead. Furthermore, we test QFDNN against six noise models, demonstrating its robustness across various error conditions. Our findings highlight QFDNN’s potential to enhance trust and security in social financial technology by accurately detecting fraudulent transactions while supporting sustainability through its resource-efficient design and minimal computational overhead.

The escalating complexity of urban transportation systems, increased by traffic congestion, diverse transportation modalities, and shifting commuter preferences, necessitates developing more sophisticated analytical frameworks. Traditional computational approaches often struggle with the voluminous datasets generated by real-time sensor networks, and they generally lack the precision needed for accurate traffic prediction and efficient system optimization. Therefore, we integrate quantum computing techniques to enhance Vehicle Road Cooperation Systems (VRCS). By leveraging quantum algorithms, specifically$UU^{\dagger }$and variational$UU^{\dagger }$, in conjunction with quantum image encoding methods such as Flexible Representation of Quantum Images (FRQI) and Novel Enhanced Quantum Representation (NEQR), we propose an optimized Quantum Neural Network (QNN). The QNN features adjustments in its entangled layer structure and training duration to handle traffic data processing complexities better. Empirical evaluations on two traffic datasets show that our model achieves superior classification accuracies of 97.42% and 84.08% and demonstrates remarkable robustness in various noise conditions. Our study underscores the potential of quantum-enhanced 6G solutions in streamlining complex transportation systems, highlighting the pivotal role of quantum technologies in advancing intelligent transportation solutions.

In transportation cyber-physical systems (CPS), ensuring safety and reliability in real-time decision-making is essential for successfully deploying autonomous vehicles and intelligent transportation networks. However, these systems face significant challenges, such as computational complexity and the ability to handle ambiguous inputs like shadows in complex environments. This paper introduces a Quantum Deep Convolutional Neural Network (QDCNN) designed to enhance the safety and reliability of CPS in transportation by leveraging quantum algorithms. At the core of QDCNN is the UU$\dagger $method, which is utilized to improve shadow detection through a propagation algorithm that trains the centroid value with preprocessing and postprocessing operations to classify shadow regions in images accurately. The proposed QDCNN is evaluated on three datasets on normal conditions and one road affected by rain to test its robustness. It outperforms existing methods in terms of computational efficiency, achieving a shadow detection time of just 0.0049352 seconds, faster than classical algorithms like intensity-based thresholding (0.03 seconds), chromaticity-based shadow detection (1.47 seconds), and local binary pattern techniques (2.05 seconds). This remarkable speed, superior accuracy, and noise resilience demonstrate QDCNN’s —key factors for safe navigation in autonomous transportation in real-time. This research demonstrates the potential of quantum-enhanced models in addressing critical limitations of classical methods, contributing to more dependable and robust autonomous transportation systems within the CPS framework.

Parameterized quantum circuits (PQCs) have emerged as a foundational element in the development and applications of quantum algorithms. However, when initialized with random parameter values, PQCs often exhibit barren plateaus (BP). These plateaus, characterized by vanishing gradients with an increasing number of qubits, hinder optimization in quantum algorithms. In this paper, we analyze the impact of state-of-the-art parameter initialization strategies from classical machine learning in random PQCs from the aspect of BP phenomenon. Our investigation encompasses a spectrum of initialization techniques, including random, Xavier (both normal and uniform variants), He, LeCun, and Orthogonal methods. Empirical assessment reveals a pronounced reduction in variance decay of gradients across all these methodologies compared to the randomly initialized PQCs. Specifically, the Xavier initialization technique outperforms the rest, showing a 62% improvement in variance decay compared to the random initialization. The He, Lecun, and orthogonal methods also display improvements, with respective enhancements of 32 %, 28 %, and 26 %. This compellingly suggests that the adoption of these existing initialization techniques holds the potential to significantly amplify the training efficacy of Quantum Neural Networks (QNNs), a subclass of PQCs. Demonstrating this effect, we employ the identified techniques to train QNNs for learning the identity function, effectively mitigating the adverse effects of BPs. The training performance, ranked from the best to the worst, aligns with the variance decay enhancement as outlined above. This paper underscores the role of tailored parameter initialization in mitigating the BP problem and eventually enhancing the training dynamics of QNNs.

By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimization as the number of factors affecting power generation and transmission rises. Extreme environmental cases will consequently lead to the imperfect functioning of Internet of Things (IoT) systems. By utilizing quantum-mechanical properties, such as superposition and entanglement, quantum computers can computationally outperform classical computers while consuming much less energy. In this article, we investigate various quantum machine learning algorithms on two data sets (TWTDUS and SDWTT18) related to IoT extreme environment and study the effect of a noisy quantum environment. We observe that for the TWTDUS data set, the variational$UU^{\dagger }$with analytical clustering methods achieves the highest accuracy of 98.10%. Similarly, for the SDWTT18 data set, the$UU^{\dagger }$method with$k$-means clustering achieves an accuracy of 94.43%. The results show that the accuracy of the proposed quantum algorithms outperforms the existing classical methods and can be utilized to forecast output power generation daily by measuring the metrics required in energy sector decision-making situations. This will be useful to save energy and costs in an IoT-extreme environment, where energy organizations must decide instantly whether to start or stop generating units.

Superdense coding (SDC) is a popular protocol demonstrating the potential of using quantum mechanics to transfer data, where The sender (Alice) can transfer 2 bits of classical information over a single qubit. We present a scheme for quantum superdense coding through Bragg diffracted hyperentangled atoms generated using cavity quantum electrodynamics (QED). In our scheme, Alice transfers 2 bits of classical information over a single hyperentangled atom. This is achieved by introducing multiple quantum gates using resonant and off-resonant Bragg diffraction in cavity QED setup. This scheme uses multiple degrees of freedom to add an extra layer of security to the encoded information.

Cross-silo federated learning (FL) allows organizations to collaboratively train machine learning (ML) models by sending their local gradients to a server for aggregation, without having to disclose their data. The main security issues in FL, that is, the privacy of the gradient and the trained model, and the correctness verification of the aggregated gradient, are gaining increasing attention from industry and academia. A popular approach to protect the privacy of the gradient and the trained model is for each client to mask their own gradients using additively homomorphic encryption (HE). However, this leads to significant computation and communication overheads. On the other hand, to verify the aggregated gradient, several verifiable FL protocols that require the server to provide a verifiable aggregated gradient were proposed. However, these verifiable FL protocols perform poorly in computation and communication. In this paper, we propose SVFL, an efficient protocol for cross-silo FL, that supports both secure gradient aggregation and verification. We first replace the heavy HE operations with a simple masking technique. Then, we design an efficient verification mechanism that achieves the correctness verification of the aggregated gradient. We evaluate the performance of SVFL and show, by complexity analysis and experimental evaluations, that its computation and communication overheads remain low even on large datasets, with a negligible accuracy loss (less than$1\%$). Furthermore, we conduct experimental comparisons between SVFL and other existing FL protocols to show that SVFL achieves significant efficiency improvements in both computation and communication.

Honeypot technologies provide an effective defense strategy for the Industrial Internet of Things (IIoT), particularly in enhancing the Advanced Metering Infrastructure’s (AMI) security by bolstering the network intrusion detection system. For this security paradigm to be fully realized, it necessitates the active participation of small-scale power suppliers (SPSs) in implementing honeypots and engaging in collaborative data sharing with traditional power retailers (TPRs). To motivate this interaction, TPRs incentivize data sharing with tangible rewards. However, without access to an SPS’s confidential data, it is daunting for TPRs to validate shared data, thereby risking SPSs’ privacy and increasing sharing costs due to voluminous honeypot logs. These challenges can be resolved by utilizing Federated Learning (FL), a distributed machine learning (ML) technique that allows for model training without data relocation. However, the conventional FL algorithm lacks the requisite functionality for both the security defense model and the rewards system of the AMI network. This work presents two solutions: first, an enhanced and cost-efficient FedAvg algorithm incorporating a novel data quality measure, and second, FedPot, the development of an effective security model with a fair incentives mechanism under an FL architecture. Accordingly, SPSs are limited to sharing the ML model they learn after efficiently measuring their local data quality, whereas TPRs can verify the participants’ uploaded models and fairly compensate each participant for their contributions through rewards. Moreover, the proposed scheme addresses the problem of harmful participants who share subpar models while claiming high-quality data through a two-step verification approach. Simulation results, drawn from realistic mircorgrid network log datasets, demonstrate that the proposed solutions outperform state-of-the-art techniques by enhancing the security model and guaranteeing fair reward distributions.

Ensuring data security in Internet of Things (IoT) networks is a major challenge due to the limited computational resources and energy constraints of these devices. Physical unclonable functions (PUFs) are hardware-based security tools that can provide unique identification for lightweight devices, making PUFs ideal for IoT applications. While PUFs have traditionally been used for authentication, in this paper, we propose a novel PUF protocol that can provide data integrity. Maintaining data integrity is crucial in many applications, including finance, healthcare, and energy. The proposed integrity based-PUF protocol is especially suitable for power constraint devices as it offers low computational overhead, execution time, and efficient communication overhead. In addition, we introduce a threat model called Physically Active Attack (PAA), a security requirement called Integrity Preserving (IP), to provide a comprehensive range of attacks and security goals specific to the context of our protocol. We then formally prove that our protocol preserves IP under PAA. We show that our proposed protocol is resistant to various attacks, including replay attacks, Man-in-the-middle attacks (MITM), cloning, brute force attacks, and physical attacks.

Vehicular communication networks are rapidly emerging as vehicles become smarter. However, these networks are increasingly susceptible to various attacks. The situation is exacerbated by the rise in automated vehicles complicates, emphasizing the need for security and authentication measures to ensure safe and effective traffic management. In this paper, we propose a novel hybrid physical layer security (PLS)-machine learning (ML) authentication scheme by exploiting the position of the transmitter vehicle as a device fingerprint. We use a time-of-arrival (ToA) based localization mechanism where the ToA is estimated at roadside units (RSUs), and the coordinates of the transmitter vehicle are extracted at the base station (BS). Furthermore, to track the mobility of the moving legitimate vehicle, we use ML model trained on several system parameters. We try two ML models for this purpose, i.e., support vector regression and decision tree. To evaluate our scheme, we conduct binary hypothesis testing on the estimated positions with the help of the ground truths provided by the ML model, which classifies the transmitter node as legitimate or malicious. Moreover, we consider the probability of false alarm and the probability of missed detection as performance metrics resulting from the binary hypothesis testing, and mean absolute error (MAE), mean square error (MSE), and coefficient of determination R2to further evaluate the ML models. We also compare our scheme with a baseline scheme that exploits angle of arrival at RSUs for authentication. We observe that our proposed position-based mechanism outperforms the baseline scheme significantly in terms of missed detections.

Advanced Information Communication Technology (ICT) is used in smart grid systems to introduce intelligence and efficiency, potentially outperforming conventional power systems. A fundamental component of a smart grid system is the Smart Meters (SMs), which are integrated with billing utilities, such as national control centers (NCC), and advanced metering infrastructure (AMI). However, like most emerging technologies, some security vulnerabilities and attacks were found. In this paper, we address such vulnerabilities, specifically associated with SMs, that occur when energy consumption is reported to the billing system, specifically through the IEC-60870-5-104(IEC-104) protocol. Since existing datasets do not include sufficient data related to such vulnerabilities, especially in SM with IEC-104 protocol communication, we developed a testbed with a virtual environment and generated a dataset with and without attack vectors. We then proposed a novel anomaly detection algorithm based on LSTM autoencoder, which combines the functional benefits of LSTM and the deep learning of autoencoders. The model's performance is evaluated against two popular attacks, MITM and Replay, and our result shows that the replay attack is harder to find since the attack is executed without data alteration.

Research in underwater communication is rapidly becoming attractive due to its various modern applications. An efficient mechanism to secure such communication is via physical layer security. In this paper, we propose a novel physical layer authentication (PLA) mechanism in underwater acoustic communication networks where we exploit the position/location of the transmitter nodes to achieve authentication. We perform transmitter position estimation from the received signals at reference nodes deployed at fixed positions in a predefined underwater region. We use time of arrival (ToA) estimation and derive the distribution of inherent uncertainty in the estimation. Next, we perform binary hypothesis testing on the estimated position to decide whether the transmitter node is legitimate or malicious. We then provide closed-form expressions of false alarm rate and missed detection rate resulted from binary hypothesis testing. We validate our proposal via simulation results, which demonstrate errors’ behavior against the link quality, malicious node location, and receiver operating characteristic (ROC) curves. We also compare our results with the performance of previously proposed fingerprint mechanisms for PLA in underwater acoustic communication networks, for which we show a clear advantage of using the position as a fingerprint in PLA.

In this paper, the secrecy of a dual-hop unmanned aerial vehicle-based wireless communication system, in the presence of mobility and imperfect channel state information (CSI), is investigated. The system consists of a decode-and-forward relay connecting a source and destination node. The transmission is performed under the presence of two eavesdroppers aiming to intercept independently the source-relay and source-destination communication channels. It is assumed that the transmitters are equipped with one transmit antenna, while the receivers have multiple receive antennas. Based on the statistical properties of the per-hop signal-to-noise ratio (SNR), a closed-form formula for the network’s secrecy intercept probability (IP) is derived, in terms of the main system and channel parameters. The results correlate the impact of such parameters on the secrecy level of the system, where the latter can be enhanced by increasing the number of antennas at the legitimate receivers and the average SNRs of the legitimate links. Furthermore, it is shown that as the CSI imperfection level, nodes’ speed, delay, and carrier frequency increase, the system’s secrecy degrades. All the derived results are verified through Monte Carlo simulations.

In this article, the physical-layer security of a dual-hop unmanned aerial vehicle-based wireless network, subject to imperfect channel state information (CSI) and mobility effects, is analyzed. Specifically, a source node$(S)$communicates with a destination node$(D)$through a decode-and-forward relay$(R)$, in the presence of two wiretappers$(E_{1}$and$E_{2}$) independently trying to compromise the two hops. Furthermore, the transmit nodes$(S,R) $have a single transmit antenna, while the receivers$(R,D,E_{1},E_{2}) $are equipped with multiple receive antennas. Based on the per-hop signal-to-noise ratios (SNRs) and correlated secrecy capacities’ statistics, a closed-form expression for the secrecy intercept probability (IP) metric is derived, in terms of key system parameters. Additionally, asymptotic expressions are revealed for two scenarios, namely: 1) mobile nodes with imperfect CSI and 2) static nodes with perfect CSI. The results show that a zero secrecy diversity order is manifested for the first scenario, due to the presence of a ceiling value of the average SNR, while the IP drops linearly at high average SNR in the second one, where the achievable diversity order depends on the fading parameters and number of antennas of the legitimate links/nodes. Furthermore, for static nodes, the system can be castigated by a 15-dB secrecy loss at IP$=3\times 10^{-3}$, when the CSI imperfection power raises from 0 to 10−3. Finally, the higher the legitimate nodes’ speed, carrier frequency, delay, and/or relay’s decoding threshold SNR, the worse is the system’s secrecy. Monte Carlo simulations endorse the derived analytical results.

Attribute-based encryption (ABE) has been widely used to provide fine-grained access control to encrypted data in cloud computing. However, in practice, since the attributes are shared by multiple users in the ABE system, it is difficult to identify malicious users which intentionally but implicitly reveal their own ABE secret keys for some purpose. In recent years, a substantial amount of work focused on designing efficient tracing mechanisms to identify such misbehaving users on ABE. In this article, we propose an efficient generic construction of black-box traceable ABE to identify the traitors. Our construction supports (public) adaptive black-box traceability and has short keys and short ciphertexts that are independent of the number of users. Technically, our construction relies on a generic transformation from any attribute-based inner-product functional encryption (ABIPFE) scheme to black-box traceable ABE system. Furthermore, to instantiate our generic construction from standard lattices, we propose the first lattice-based ABIPFE scheme, which is proven weakly selective secure in the standard model.

Social media plays a significant role in sharing essential information, which helps humanitarian organizations in rescue operations during and after disaster incidents. However, developing an efficient method that can provide rapid analysis of social media images in the early hours of disasters is still largely an open problem, mainly due to the lack of suitable datasets and the sheer complexity of this task. In addition, supervised methods can not generalize well to novel disaster incidents. In this paper, inspired by the success of Knowledge Distillation (KD) methods, we propose an unsupervised deep neural network to detect and localize damages in social media images. Our proposed KD architecture is a feature-based distillation approach that comprises a pre-trained teacher and a smaller student network, with both networks having similar GAN architecture containing a generator and a discriminator. The student network is trained to emulate the teacher’s behavior on training input samples, which, in turn, contain images that do not include any damaged regions. Therefore, the student network only learns the distribution of no damage data and would have different behavior from the teacher network facing damages. To detect damage, we utilize the difference between features generated by two networks using a defined score function that demonstrates the probability of damages occurring. Our experimental results on the benchmark dataset confirm that our approach outperforms state-of-the-art methods in detecting and localizing the damaged areas, especially for novel disaster types1.

Securing traffic flows in internet of vehicles (IoV) environments for connected and automated vehicles (CAVs) is a critical task as it should be done in real-time to allow vehicles’ controllers engagement on time. In this paper, the security of CAV communication at merging points is studied, the insecure vehicle communication is analysed in terms of the possible security threats and consequences, and security goals are then identified to protect the environment. We present a network topology that improves the availability of the system and propose a high-level design of a vehicle authentication protocol based on public key cryptography to authenticate vehicles. Simulation and analysis of the cryptographic functions are done to choose the best fit for vehicle communication, where Rivest-Shamir-Adleman (RSA)-2048 algorithms provide faster and more efficient computations.

Abstract Provable Data Possession (PDP) is of crucial importance in public cloud storage since it allows users to check the integrity of their outsourced data without downloading it. However, the existing PDP schemes, which are based on classical number-theoretic assumptions, are insecure under quantum attacks. In this paper, we propose the first PDP scheme from standard lattices, using a specific leveled fully homomorphic signature (FHS) scheme. To remove the complex key management of PDP cryptosystem on the public key infrastructure (PKI) setting, we employ a specific leveled identity-based (ID-based) FHS scheme to construct the first ID-based PDP scheme from standard lattices. Our two PDP schemes are secure under the standard small integer solution (SIS) assumption, which is conjectured to withstand quantum attacks. Furthermore, we conduct experimental evaluations to validate the feasibility of the proposed PDP schemes in practice.

Homomorphic Proxy Re-Encryption (HPRE) is an extension of Proxy Re-Encryption (PRE) which combines the advantages of both Homomorphic Encryption (HE) and PRE. A HPRE scheme allows arbitrary evaluations to be performed on ciphertexts under one (the delegator's) public key and, using a re-encryption key, it transforms the resulting ciphertext to a new ciphertext under another (the delegatee's) public key. Prior HPRE schemes are either CPA-secure or CCA-secure but only support partial homomorphic operations. We propose a generic construction of single-hop HPRE scheme which supports fully homomorphic operations. The proposed scheme is proven secure in our new index-based CCA-HPRE model. Our technique is to give a generic transformation that turns any multi-identity identity-based FHE (IBFHE) scheme with key switching into Fully Homomorphic Encryption (FHE) with key switching from which we can obtain the proposed single-hop HPRE scheme. We also present a concrete instantiation of multi-identity IBFHE with key switching from learning with errors (LWE) in the standard model.

Cyber education development is a crucial issue considering the human resource and skill shortage in the current cybersecurity arena. A cyber range is a tried and tested hands-on training in cybersecurity education, providing threat simulation of various scenarios. However, the threat scenario development poses crucial challenges that hurt the learning process and trainee's engagement in training. Firstly, the threat scenarios are static and have limited applicability. Secondly, due to the lack of proper representation of procedures and training scenarios used in attacks, it is hard to recognize redundant procedures. We propose an Attack Specific Language (ASL) based on the Mitre ATT&CK framework. It provides one representation for all threat scenarios. This language will give information about attack techniques in compact ways, which will streamline and automate the cyber range functions of threat and challenge execution. It will help identify and reduce redundancy. ASL will also provide training customization through dynamic threat execution, which will be trainee-aware and will consider the trainee's performance while executing scenarios. It will provide trainees, better engagement, and training experience.