Search Results (2,157)

Beluga whale optimization (BWO) is a swarm-based metaheuristic algorithm inspired by the group behavior of beluga whales. BWO suffers from drawbacks such as an insufficient exploration capability and the tendency to fall into local optima. To address these shortcomings, this paper proposes augmented multi-strategy beluga optimization (AMBWO). The adaptive population learning strategy is proposed to improve the global exploration capability of BWO. The introduction of the roulette equilibrium selection strategy allows BWO to have more reference points to choose among during the exploitation phase, which enhances the flexibility of the algorithm. In addition, the adaptive avoidance strategy improves the algorithm’s ability to escape from local optima and enriches the population quality. In order to validate the performance of the proposed AMBWO, extensive evaluation comparisons with other state-of-the-art improved algorithms were conducted on the CEC2017 and CEC2022 test sets. Statistical tests, convergence analysis, and stability analysis show that the AMBWO exhibits a superior overall performance. Finally, the applicability and superiority of the AMBWO was further verified by several engineering optimization problems. Full article

Quantum communication holds great potential for enhancing the security and efficiency of the Internet of Things (IoT). However, existing schemes often overlook device identity authentication, leaving systems vulnerable to unauthorized access, and rely on third-party controllers, which increase complexity and undermine trust. This paper proposes a novel asymmetric bidirectional quantum communication scheme tailored for IoT, integrating device identity authentication and information transmission without requiring third-party controllers. We provide a detailed description of the scheme’s application scenarios in IoT, conduct a security analysis of the identity authentication module, and experimentally validate the feasibility of the information transmission module. Additionally, we analyze the impact of quantum noise on the proposed scheme and compare it with existing approaches, highlighting its advantages in terms of resource consumption and efficiency. Full article

The Internet of Things (IoT) has been a major part of many use cases for 5G networks. From several of these use cases, it follows that 5G should be able to support at least one million devices per km². In this paper, we explain that the 5G radio access schemes as used today cannot support such densities. This issue will have to be solved by 6G. However, this requires a fundamentally different approach to accessing the wireless medium compared to current generation networks: they are not designed to support many thousands of devices in each other’s vicinity, attempting to send/receive data simultaneously. In this paper, we present a 6G system architecture for trading wireless network resources in massive IoT scenarios, inspired by the concept of the sharing economy, and using the novel concept of spectrum programming. We simulated a truly massive IoT network and evaluated the scalability of the system when managed using our proposed 6G platform, compared to standard 5G deployments. The experiments showed how the proposed scheme can improve network resource allocation by up to 80%. This is accompanied by similarly significant improvements in interference and device energy consumption. Finally, we performed evaluations that demonstrate that the proposed platform can benefit all the stakeholders that decide to join the scheme. Full article

The scope of this work is to propose a distributed RF sensing architecture that interconnects and utilizes a cyber security operations center (SOC) to support long-term RF threat monitoring, alerting, and further centralized processing. For the purpose of this work, RF threats refer mainly to RF jamming, since this can jeopardize multiple wireless systems, either directly as a Denial of Service (DoS) attack, or as a means to force a cellular or WiFi wireless client to connect to a malicious system. Furthermore, the possibility of the suggested architecture to monitor signals from malicious drones in short distances is also examined. The work proposes, develops, and examines the performance of RF sensing sensors that can monitor any frequency band within the range of 1 MHz to 8 GHz, through selective band pass RF filtering, and subsequently these sensors are connected to a remote SOC. The proposed sensors incorporate an automatic calibration and time-depended environment RF profiling algorithm and procedure for optimizing RF jamming detection in a dense RF spectrum, occupied by heterogeneous RF technologies, thus minimizing false-positive alerts. The overall architecture supports TCP/IP interconnections of multiple RF jamming detection sensors through an efficient MQTT protocol, allowing the collaborative operation of sensors that are distributed in different areas of interest, depending on the scenario of interest, offering holistic monitoring by the centralized SOC. The incorporation of the centralized SOC in the overall architecture allows also the centralized application of machine learning algorithms on all the received data. Full article

By integrating advanced technologies and data-driven systems in smart grids, there has been a significant revolution in the energy distribution sector, bringing a new era of efficiency and sustainability. Nevertheless, with this advancement comes vulnerability, particularly in the form of cyber threats, which have the potential to damage critical infrastructure. False data injection attacks are among the threats to the cyber–physical layer of smart grids. False data injection attacks pose a significant risk, manipulating the data in the control system layer to compromise the grid’s integrity. An early detection and mitigation of such cyberattacks are crucial to ensuring the smart grid operates securely and reliably. In this research paper, we demonstrate different machine learning classification models for detecting false data injection attacks, including the Extra Tree, Random Forest, Extreme Gradient Boosting, Logistic Regression, Decision Tree, and Bagging Classifiers, to secure the integrity of smart grids. A comprehensive dataset of various attack scenarios provides insights to explore and develop effective detection models. Results show that the Extra Tree, Random Forest, and Extreme Gradient Boosting models’ accuracy in detecting the attack outperformed the existing literature, an achieving accuracy of 98%, 97%, and 97%, respectively. Full article

The article examines modern approaches to energy management in the context of the development of Industry 5.0 with a particular focus on cybersecurity. Key tenets of Industry 5.0 are discussed, including the integration of advanced technologies with intelligent energy management systems (IEMSs) and the growing need to protect data in the face of increasing cyber threats. The challenges faced by small and medium-sized enterprises (SMEs) using solutions based on renewable energy sources, such as photovoltaic farms, are also analyzed. The article presents examples of IEMS applications and discusses methods for securing these systems, offering an overview of cyber threat protection tools in the context of modern energy management. The analysis carried out provided information that will help businesses make rational decisions and contribute to shaping the state’s macroeconomic policy on cybersecurity and energy savings. The results of this research can also help develop more effective strategies for managing technology and IT infrastructure, which is crucial in the digital age of Industry 5.0. Full article

In today’s healthcare industry, safeguarding patient data is critical due to the increasing digitization of medical records, which makes them vulnerable to cyber threats. Telehealth services, while providing immense benefits in terms of accessibility and efficiency, introduce complex challenges in maintaining data privacy and security. This paper proposes a privacy-preserving framework for secure data sharing within telehealth services, employing blockchain technology and advanced cryptographic techniques. The framework ensures that all patient health data are encrypted using homomorphic encryption before storage on the blockchain, guaranteeing confidentiality and protecting data from unauthorized access. Secure multi-party computation (SMPC) is integrated for encrypted data computations, maintaining data confidentiality even during operations. Smart contracts enforce access control, ensuring that patient preferences and regulatory requirements such as the HIPAA and the GDPR are met. Furthermore, the framework includes auditing and verifying data integrity mechanisms, making it resilient against cyber threats such as impersonation, replay, and Man-In-The-Middle attacks. The analysis demonstrates the framework’s superior performance in addressing these challenges compared to that of existing systems. Future work suggests integrating AI-driven threat detection and quantum-resistant cryptographic techniques to enhance security further and adapt to the evolving telehealth landscape. Full article

Electric vehicles (EVs) and their ecosystem have unquestionably made significant technological strides. Indeed, EVs have evolved into sophisticated computer systems with extensive internal and external communication capabilities. This interconnection raises concerns about security, privacy, and the expanding risk of cyber-attacks within the electric vehicle landscape. In particular, the charging infrastructure plays a crucial role in the electric mobility ecosystem. With the proliferation of charging points, new attack vectors are opened up for cybercriminals. The threat landscape targeting charging systems encompasses various types of attacks ranging from physical attacks to data breaches including customer information. In this paper, we aim to leverage the power of model-driven engineering to model and analyze EV charging systems at early stages. We employ domain-specific modeling language (DSML) techniques for the early security modeling and analysis of EV charging infrastructure. We accomplish this by integrating the established EMSA model for electric mobility, which encapsulates all key stakeholders in the ecosystem. To our knowledge, this represents the first instance in the literature of applying DSML within the electric mobility ecosystem, highlighting its innovative nature. Moreover, as our formalization based on DSML is an iterative, continuous, and evolving process, this approach guarantees that our proposed framework adeptly tackles the evolving cyber threats confronting the EV industry. Specifically, we use the Object Constraint Language (OCL) for precise specification and verification of security threats as properties of a modeled system. To validate our framework, we explore a set of representative threats targeting EV charging systems from real-world scenarios. To the best of our knowledge, this is the first attempt to provide a comprehensive security modeling framework for the electric mobility ecosystem. Full article

Vehicular Ad-Hoc Networks (VANETs) play an essential role in the intelligent transportation era, furnishing users with essential roadway data to facilitate optimal route selection and mitigate the risk of accidents. However, the network exposure makes VANETs susceptible to cyber threats, making authentication crucial for ensuring security and integrity. Therefore, joining entity verification is essential to ensure the integrity and security of communication in VANETs. However, to authenticate the entities, authentication time should be minimized to guarantee fast and secure authentication procedures. We propose an authentication system for VANETs using blockchain and Kerberos for storing authentication messages in a blockchain ledger accessible to Trusted Authentication Servers (TASs) and Roadside Units (RSUs). We evaluate the system in three diverse network scenarios: suburban, urban with 1 TAS, and urban with 2 TASs. The findings reveal that this proposal is applicable in diverse network scenarios to fulfill the network requirements, including authentication, handover, and end-to-end delay, considering an additional TAS for an increasing number of vehicles. The system is also practicable in storing the authentication message in blockchain considering the gas values and memory size for all scenarios. Full article

Practical challenges and special threats from scam-related fraud exist for regional and local communities in Southeast Asia during and after the COVID-19 pandemic. The rise in pig-butchering operations in Southeast Asia is a major concern due to the increased use of digital technology and online financial transactions. Many of these operations are linked to organized crime syndicates operating across borders, posing challenges for law enforcement. As a first study in Vietnam, we combined the primary and secondary databases to unveil the nature of transnational scam-related fraud. Findings show that scammers are using advanced methods such as phishing, fraudulent investments, and identity theft to maximize their sophisticated tactics for achieving financial possession. There are organized crime rings operating in Vietnam and Cambodia, with Chinese groups playing a leading role behind the scenes. Social media and its various applications have become common platforms for these criminal activities. This study also calls for practical recommendations to consider specific challenges in combating these crimes, including building a strong framework with clear policies, encouraging multiple educational awareness campaigns in communities, enhancing effective cooperation among law enforcement and others, and supporting evidence-based approaches in research and application. While we recognized and assumed that pig-butchering operations with scam-related fraud are a complex problem that requires a well-rounded and coordinated response, the exact approach would depend on each country’s specific circumstances. Full article

This article explores the challenges of integrating two deep learning neural networks, YOLOv5 and RT-DETR, to enhance the recognition of unmanned aerial vehicles (UAVs) within the optical-electronic channels of Sensor Fusion systems. The authors conducted an experimental study to test YOLOv5 and Faster RT-DETR in order to identify the average accuracy of UAV recognition. A dataset in the form of images of two classes of objects, UAVs, and birds, was prepared in advance. The total number of images, including augmentation, amounted to 6337. The authors implemented training, verification, and testing of the neural networks exploiting PyCharm 2024 IDE. Inference testing was conducted using six videos with UAV flights. On all test videos, RT-DETR-R50 was more accurate by an average of 18.7% in terms of average classification accuracy (Pc). In terms of operating speed, YOLOv5 was 3.4 ms more efficient. It has been established that the use of RT-DETR as the only module for UAV classification in optical-electronic detection channels is not effective due to the large volumes of calculations, which is due to the relatively large number of parameters. Based on the obtained results, an algorithm for combining two neural networks is proposed, which allows for increasing the accuracy of UAV and bird classification without significant losses in speed. Full article

Today, many businesses use near-field communications (NFC) payment solutions, which allow them to receive payments from customers quickly and smoothly. However, this technology comes with cyber security risks which must be analyzed and mitigated. This study explores the cyber risks associated with NFC transactions and examines strategies for mitigating these risks, focusing on payment devices. This paper provides an overview of NFC technology, related security vulnerabilities, privacy concerns, and fraudulent activities. It then investigates payment devices such as smartphones, contactless cards, and wearables, highlighting their features and vulnerabilities. The study also examines encryption, authentication, tokenization, biometric authentication, and fraud detection methods as risk mitigation strategies. The paper synthesizes theoretical frameworks to provide insights into NFC transaction security and offers stakeholder recommendations. Full article

The Internet of Medical Things (IoMT), an extension of the Internet of Things (IoT), is still in its early stages of development. Challenges that are inherent to IoT, persist in IoMT as well. The major focus is on data transmission within the healthcare domain due to its profound impact on health and public well-being. Issues such as latency, bandwidth constraints, and concerns regarding security and privacy are critical in IoMT owing to the sensitive nature of patient data, including patient identity and health status. Numerous forms of cyber-attacks pose threats to IoMT networks, making the reliable and secure transmission of critical medical data a challenging task. Several other situations, such as natural disasters, war, construction works, etc., can cause IoMT networks to become unavailable and fail to transmit the data. The first step in these situations is to recover from failure as quickly as possible, resume the data transfer, and detect the cause of faults, failures, and errors. Several solutions exist in the literature to make the IoMT resilient to failure. However, no single approach proposed in the literature can simultaneously protect the IoMT networks from various attacks, failures, and faults. This paper begins with a detailed description of IoMT and its applications. It considers the underlying requirements of resilience for IoMT networks, such as monitoring, control, diagnosis, and recovery. This paper comprehensively analyzes existing research efforts to provide IoMT network resilience against diverse causes. After investigating several research proposals, we identify that the combination of software-defined networks (SDNs), machine learning (ML), and microservices architecture (MSA) has the capabilities to fulfill the requirements for achieving resilience in the IoMT networks. It mainly focuses on the analysis of technologies, such as SDN, ML, and MSA, separately, for meeting the resilience requirements in the IoMT networks. SDN can be used for monitoring and control, and ML can be used for anomaly detection and diagnosis, whereas MSA can be used for bringing distributed functionality and recovery into the IoMT networks. This paper provides a case study that describes the remote patient monitoring (RPM) of a heart patient in IoMT networks. It covers the different failure scenarios in IoMT infrastructure. Finally, we provide a proposed methodology that elaborates how distributed functionality can be achieved during these failures using machine learning, software-defined networks, and microservices technologies. Full article

Cyber security is supportive of robotic service provision, the objective of which is to help marketers achieve their aim of providing a high level of service. Marketers need to be aware of cyber security issues and adhere to established cyber security policies. We investigate trust and commitment in relation to customer retention while assessing the mediating role of robotic service quality (RSQ). We employ a survey-based study that utilises 231 valid responses from customers in São Paulo, Brazil. To analyse the data, we used partial least squares structural equation modelling (PLS-SEM). The results show that trust and commitment have a positive impact on customer retention. RSQ has a partial mediation effect on the relationship between the latent constructs of trust, commitment, and customer retention. Thus, it can be suggested that RSQ, which embeds trust and commitment, assists in building a loyal customer base. Marketers outside the Latin American region can benefit from the results of this study since it incorporates cyber security awareness and policy within marketing strategy implementation, ensuring that RSQ is aligned in terms of the digitalisation goals of the company. Full article

In network security, intrusion detection systems (IDSs) are essential for maintaining network integrity. Traditional IDSs primarily use supervised learning, relying on extensive datasets for effective training, which limits their ability to address rapidly evolving cyber threats, especially with limited data samples. To overcome this, prior research has applied meta-learning methods to distinguish between normal and malicious network traffic, showing promising results mainly in binary classification scenarios. However, challenges remain in model information acquisition within few-shot learning (FSL) frameworks. This study introduces a metric-based meta-learning strategy that constructs prototypes for each sample category, improving the model’s ability to manage multi-class scenarios. Additionally, we propose an Adaptive Feature Fusion (AFF) mechanism that dynamically integrates statistical features and binary data streams to extract meaningful insights from limited datasets, thereby enhancing the effectiveness of IDSs in few-shot learning contexts. By introducing a metric-based meta-learning method and the Adaptive Feature Fusion mechanism, this study provides a feasible solution for developing a high-accuracy, multi-class few-shot intrusion detection system. A series of experiments show that this approach significantly improves the effectiveness of the intrusion detection system, achieving an impressive accuracy of 97.78% in multi-class tasks, even when the sample size is reduced to just one. Full article