The Kingdom of Saudi Arabia (KSA) has achieved significant milestones in cybersecurity. KSA has maintained solid regulatory mechanisms to prevent, trace, and punish offenders to protect the interests of both individual users and organizations from the online threats of data poaching and pilferage. The widespread usage of Information Technology (IT) and IT Enable Services (ITES) reinforces security measures. The constantly evolving cyber threats are a topic that is generating a lot of discussion. In this league, the present article enlists a broad perspective on how cybercrime is developing in KSA at present and also takes a look at some of the most significant attacks that have taken place in the region. The existing legislative framework and measures in the KSA are geared toward deterring criminal activity online. Different competency models have been devised to address the necessary cybercrime competencies in this context. The research specialists in this domain can benefit more by developing a master competency level for achieving optimum security. To address this research query, the present assessment uses the Fuzzy Decision-Making Trial and Evaluation Laboratory (Fuzzy-DMTAEL), Fuzzy Analytic Hierarchy Process (F.AHP), and Fuzzy TOPSIS methodology to achieve segment-wise competency development in cyber security policy. The similarities and differences between the three methods are also discussed. This cybersecurity analysis determined that the National Cyber Security Centre got the highest priority. The study concludes by perusing the challenges that still need to be examined and resolved in effectuating more credible and efficacious online security mechanisms to offer a more empowered ITES-driven economy for Saudi Arabia. Moreover, cybersecurity specialists and policymakers need to collate their efforts to protect the country’s digital assets in the era of overt and covert cyber warfare.

A private permissioned chain is typically considered a fully protected authorized blockchain. The concept of a permissioned chain has become better single-entity control operation over the network and allows entry only to selected nodes, which most probably supports different small-medium enterprises and government officials. It is one of the prominent features of blockchain technology that maintains transaction integrity and security throughout the deliverance. This paper initially highlights the benefits of a permissioned private chain. Present the current issues of the Proof-of-Elapsed Time consensus mechanism for the distributed applicational environment. In order to make blockchain systems more adaptive for private chains, this paper proposes a middleware lightweight consensus based on PoET, named “B-LPoET”. Objecting to being improved as a lightweight PoET from the predefined PoET in terms of verifying transactions and creating more blocks, to decide the mining right, and participants allows to acknowledge their identities before joining. The design of B-LPoET reduces the cost of multi-node efficiency by establishing a lightweight topology for selecting and waiting time of winning node using multithreading, which affects positively by increasing systems scalability. The experimental results of the B-LPoET illustrate that it performs better as compared with the existing PoET and other state-of-the-art consensus.

Autonomous Vehicles (AVs) have revolutionized transportation by utilizing 6G technologies such as automated driving assistance, navigation, connected intelligence, and independent decision-making. Yet, the increasing reliance on AVs exposes the Internet of Vehicles (IoV) to potential vulnerabilities, making it susceptible to cyber attacks. One prominent threat is Distributed Denial of Service (DDoS) attacks, which can significantly impact AVs' safety and operational integrity. DDoS attacks directly disrupt the fundamental functionality of AVs to make timely and informed decisions, potentially leading to accidents or system failures. Despite the existence of numerous systems for detecting DDoS attacks, their continuous evolution in various attack patterns poses a significant challenge for effective detection. This paper provides a vision of 6G Security by proposing an Advanced DDoS Attack Detection System (ADADS) to enhance the detection capabilities of DDoS attacks by employing a Hybrid Detection Model (HDM) and a Continuous Learning Model (CLM) to adapt the evolving patterns of DDoS attacks over time dynamically. The collaborative integration of these models leverages the overall efficiency of DDoS attack detection, delivering a robust and adaptive defense mechanism. The experimental findings reveal that the proposed ADADS achieves a remarkable accuracy 98.7% with rapid stabilization in a few iterations for the current 6G specifications and applications.

We propose in this paper a novel reliable detection method to recognize forged inpainting images. Detecting potential forgeries and authenticating the content of digital images is extremely challenging and important for many applications. The proposed approach involves developing new probabilistic support vector machines (SVMs) kernels from a flexible generative statistical model named “bounded generalized Gaussian mixture model”. The developed learning framework has the advantage to combine properly the benefits of both discriminative and generative models and to include prior knowledge about the nature of data. It can effectively recognize if an image is a tampered one and also to identify both forged and authentic images. The obtained results confirmed that the developed framework has good performance under numerous inpainted images.

The persistent evolution of cyber threats has given rise to Gen V Multi-Vector Attacks, complex and sophisticated strategies that challenge traditional security measures. This research provides a complete investigation of recent intrusion detection systems designed to mitigate the consequences of Gen V Multi-Vector Attacks. Using the Fuzzy Analytic Hierarchy Process (AHP) and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), we evaluate the efficacy of several different intrusion detection techniques in adjusting to the dynamic nature of sophisticated cyber threats. The study offers an integrated analysis, taking into account criteria such as detection accuracy, adaptability, scalability, resource effect, response time, and automation. Fuzzy AHP is employed to establish priority weights for each factor, reflecting the nuanced nature of security assessments. Subsequently, TOPSIS is employed to rank the intrusion detection methods based on their overall performance. Our findings highlight the importance of behavioral analysis, threat intelligence integration, and dynamic threat modeling in enhancing detection accuracy and adaptability. Furthermore, considerations of resource impact, scalability, and efficient response mechanisms are crucial for sustaining effective defense against Gen V Multi-Vector Attacks. The integrated approach of Fuzzy AHP and TOPSIS presents a strong and adaptable strategy for decision-makers to manage the difficulties of evaluating intrusion detection techniques. This study adds to the ongoing discussion about cybersecurity by providing insights on the positive and negative aspects of existing intrusion detection systems in the context of developing cyber threats. The findings help organizations choose and execute intrusion detection technologies that are not only effective against existing attacks, but also adaptive to future concerns provided by Gen V Multi-Vector Attacks.

The Internet of Things (IoT) is one of the key components of the ICT infrastructure of smart cities due to its great potential for intelligent management of infrastructures and facilities and the enhanced delivery of services in support of sustainable cities. Smart cities typically rely on IoT, where a wide variety of devices communicate with each other and collaborate across heterogeneous and distributed computing environments to provide information and services to urban entities and urbanites. However, leveraging the IoT within software applications raises tremendous challenges, such as data acquisition, device heterogeneity, service management, security and privacy, interoperability, scalability, flexibility, data processing, and visualization. Middleware for IoT has been recognized as the system that can provide the necessary infrastructure of services and has become increasingly important for IoT over the last few years. This study aims to review and synthesize the relevant literature to identify and discuss the core challenges of existing IoT middleware. Furthermore, it augments the information landscape of IoT middleware with big data applications to achieve the required level of services supporting sustainable cities. In doing so, it proposes a novel IoT middleware for smart city applications, namely Generic Middleware for Smart City Applications (GMSCA), which brings together many studies to further capture and invigorate the application demand for sustainable solutions which IoT and big data can offer. The proposed middleware is implemented, and its feasibility is assessed by developing three applications addressing various scenarios. Finally, the GMSCA is tested by conducting load balance and performance tests. The results prove the excellent functioning and usability of the GMSCA.

Animal poaching poses a significant threat to wild animals, resulting in a rapid decrease in their populations. Unmanned Aerial Vehicles (UAVs) are extensively used to tackle illegal poaching. However, many potential security threats exist concerning transferring a huge amount of data between UAVs and forest officials. To address these challenges and enable secure transmission of big data from the UAVs, a Thermal Vision-based UAV and Blockchain (TVUB) aided poaching prevention system has been proposed in this paper. The TVUB system deploys a UAV swarm equipped with heat-sensing Thermal Infrared Radiation (TIR) sensors that run a Convolutional Neural Network (CNN)-based YOLOv4 image recognition model. The Deep Learning (DL) model is used to detect the presence of poachers based on their thermal images. Furthermore, the system deploys a novel blockchain-CNN mechanism, incorporating a smart contract that initializes the CNN framework through a serialized version of the YOLOv4 model. The execution of the poacher detection model proceeds in a decentralized manner due to the blockchain mechanism, thereby enhancing the security of big data transmitted. Extensive performance evaluation demonstrated the effective working of the TVUB system, which detected poachers with an accuracy of 96.4%.

Numerous cyberattacks on connected control systems are being reported every day. Such control systems are subject to hostile external attacks due to their communication system. Network security is vital because it protects sensitive information from cyber threats and preserves network operations and trustworthiness. Multiple safety solutions are implemented in strong and reliable network security plans to safeguard users and companies from spyware and cyber attacks, such as distributed denial of service attacks. A crucial component that must be conducted prior to any security implementation is a security analysis. Because cyberattack encounters in power control networks are currently limited, a comprehensive security evaluation approach for power control technology in communication networks is required. According to previous studies, the challenges of security evaluation include a power control process security assessment as well as the security level of every control phase. To address such issues, the fuzzy technique for order preference by similarity to ideal solution (TOPSIS) based on multiple criteria decision-making (MCDM) is presented for a security risk assessment of the communication networks of energy management and control systems (EMCS). The methodology focuses on quantifying the security extent in each control step; in order to value the security vulnerability variables derived by the protection analysis model, an MCDM strategy incorporated as a TOPSIS is presented. Ultimately, the example of six communication networks of a power management system is modelled to conduct the security evaluation. The outcome validates the utility of the security evaluation.

Effective communication in nursing, particularly with older patients, is critical to providing high-quality care. The purpose of this research is to fill key gaps in the existing literature by emphasizing the importance of therapeutic communication in the setting of mental nursing care for elderly patients in Saudi Arabia. Building on the study’s foundation, which recognizes the various issues faced by cultural, religious, and linguistic diversity, this research adopted a rigorous research methodology incorporating a broad group of senior healthcare professionals as experts. We analyze various therapeutic communication approaches used by mental health nurses using extensive surveys and observations. This empirical study’s findings are likely to make a significant addition to the field by throwing light on the most efficient methods for improving nurse–elderly-patient communication. The study identifies Simulation-Based Training as the most viable technique, with potentially far-reaching implications for improving care for older patients in Saudi Arabia. This study paves the way for significant advances in healthcare practices, with a focus on mental health nursing, ultimately helping both nurses and elderly patients by developing trust, understanding, and increased communication.

Vehicle-to-vehicle energy trading has become one of the most popular charge-sharing systems nowadays. It allows energy transfer between electric vehicles without being necessarily relied on infrastructure-based charging stations. However, the demands-offers matching and energy transportation between the vehicles remain challenging issues while considering vehicles– space and temporal location, their dynamicity, availability, and reliability. This paper addresses these issues by proposing a framework of energy trading based on blockchain and smart contracts. The energy transfer between vehicles is performed via a distributed coalition of unmanned aerial vehicles transporting the electric energy from selected sellers to a needy requester vehicle. The selection mechanism of sellers aims to maximize the service availability and fault-tolerance and minimize both the energy transportation latency and overhead. We modeled the selection process by a 0-1 knapsack problem, which we relaxed using a dynamic protocol of energy negotiation, and then developed a linear approach for its resolution. The seller reliability assessment is addressed by the proposition of a trust management approach, which evaluates over time the quality of participants regarding their history of transactions. We conducted intensive simulations with a comparison to the exact solution of resolution. The obtained results show a reduction of 42% of charging latency, an improvement of 24% of service availability, a 96% of approximation from the exact resolution, and an increase of up to 62% of robustness against unfulfilled commitments.

Security and privacy issues were long a subject of concern with drones from the past few years. This is due to the lack of security and privacy considerations in the design of the drone, which includes unsecured wireless channels and insufficient computing capability to perform complex cryptographic algorithms. Owing to the extensive real-time applications of drones and the ubiquitous wireless connection of beyond 5G (B5G) networks, efficient security measures are required to prevent unauthorized access to sensitive data. In this article, we proposed a resource-friendly proxy signcryption scheme in certificateless settings. The proposed scheme was based on elliptic curve cryptography (ECC), which has a reduced key size, i.e., 80 bits, and is, therefore, suitable for drones. Using the random oracle model (ROM), the security analysis of the proposed scheme was performed and shown to be secure against well-known attacks. The performance analysis of the proposed scheme was also compared to relevant existing schemes in terms of computation and communication costs. The findings validate the practicability of the proposed scheme.

Cognitive radio (CR) has emerged as one of the most investigated techniques in wireless networks. Research is ongoing in terms of this technology and its potential use. This technology relies on making full use of the unused spectrum to solve the problem of the spectrum shortage in wireless networks based on the excessive demand for spectrum use. While the wireless network technology node’s range of applications in various sectors may have security drawbacks and issues leading to deteriorating the network, combining it with CR technology might enhance the network performance and improve its security. In order to enhance the performance of the wireless sensor networks (WSNs), a lightweight authentication medium access control (MAC) protocol for CR-WSNs that is highly compatible with current WSNs is proposed. Burrows–Abadi–Needham (BAN) logic is used to prove that the proposed protocol achieves secure and mutual authentication. The automated verification of internet security protocols and applications (AVISPA) simulation is used to simulate the system security of the proposed protocol and to provide formal verification. The result clearly shows that the proposed protocol is SAFE under the on-the-fly model-checker (OFMC) backend, which means the proposed protocol is immune to passive and active attacks such as man-in-the-middle (MITM) attacks and replay attacks. The performance of the proposed protocol is evaluated and compared with related protocols in terms of the computational cost, which is 0.01184 s. The proposed protocol provides higher security, which makes it more suitable for the CR-WSN environment and ensures its resistance against different types of attacks.

The integration of the Internet of Things (IoT) and the telecare medical information system (TMIS) enables patients to receive timely and convenient healthcare services regardless of their location or time zone. Since the Internet serves as the key hub for connection and data sharing, its open nature presents security and privacy concerns and should be considered when integrating this technology into the current global healthcare system. Cybercriminals target the TMIS because it holds a lot of sensitive patient data, including medical records, personal information, and financial information. As a result, when developing a trustworthy TMIS, strict security procedures are required to deal with these concerns. Several researchers have proposed smart card-based mutual authentication methods to prevent such security attacks, indicating that this will be the preferred method for TMIS security with the IoT. In the existing literature, such methods are typically developed using computationally expensive procedures, such as bilinear pairing, elliptic curve operations, etc., which are unsuitable for biomedical devices with limited resources. Using the concept of hyperelliptic curve cryptography (HECC), we propose a new solution: a smart card-based two-factor mutual authentication scheme. In this new scheme, HECC’s finest properties, such as compact parameters and key sizes, are utilized to enhance the real-time performance of an IoT-based TMIS system. The results of a security analysis indicate that the newly contributed scheme is resistant to a wide variety of cryptographic attacks. A comparison of computation and communication costs demonstrates that the proposed scheme is more cost-effective than existing schemes.

Machine learning are vulnerable to the threats. The Intruders can utilize the malicious nature of the nodes to attack the training dataset to worsen the process and manipulate the learning and make the over all system with less efficiency and performance. The optimized poison attack procedures are already introduced to estimate the overall bad scenario, design the intrusion as bi‐level optimization and it is considered computational complexity is high and demanding, in contrary the applicability is limited such models deep neural networks. In this research papers, we have proposed, novel proposed system, poisoning attacks against the Machine learning training dataset, including the genuine data points that reduce the accuracy of the classifier in the process of training. The proposed system have 3 components of Generative Adverserial networks (GAN) generator, discriminator, and the target classifier. The proposed system allows to detect the vulnerability easy and it can be found as similar as realistic attacks to detect the area where the underlying data distribution have more possibility of poising attack which cause vulnerability to the network. Our experimentation, proves the claim our that the proposed model is effective on compromising the classifiers uses the machine learning algorithms and also deep learning networks.

In addition to standard authentication and data confidentiality requirements, Cognitive Radio Networks (CRNs) face distinct security issues such as primary user emulation and spectrum management attacks. A compromise of these will result in a denial of service, eavesdropping, forgery, or replay attack. These attacks must be considered while designing a secure media access control (MAC) protocol for CR networks. This paper presents a novel secure CR MAC protocol: the presented protocol is analysed for these security measures using formal logic methods such as Burrows-Abadi-Needham (BAN) logic. It is shown that the proposed protocol functions effectively to provide strong authentication and detection against malicious users leading to subsequent secure communication.

Distributed transactions in e-Healthcare and the evaluation of medical data have become an active research area of information technology that delivers medical records management and optimization without manually visualizing the computational loss. The increased use of e-Healthcare applications for availing medical services requires efficient computation during the processing of medical transactions and preservation through intelligent measurement analysis. Medical industries often involve and aim for the smooth application of medical transmission of demanding services. Thus, there are significant requirements for calculating loss during optimization and management in the distributed private network. In this paper, we contribute to two different objectives. First, we propose a machine learning-based stochastic gradient descent method for managing medical records and optimizing day-to-day transactions of e-Healthcare applications. This approach evaluates the loss of medical features during computation and enables optimized details of data transmission. Secondly, a blockchain-distributed E-Healthcare novel and a secure serverless architecture are proposed for the medical industry to protect transactions and preserve immutable storage. The simulation result shows the proposed system computations, such as loss = 0.7 (7%), learning-rate = goldilocks, ledger optimization =0.23 (23%), transmission power =-18 dBm, jitter = 32 ms, delay =90 ms, throughput = 170 bytes, duty-cycle and delivery = 0.10(10%), and calculate dynamic response.

In vehicle ad-hoc networks, the progression of wireless communication technology to 6G, overcomes storage, processing, privacy, and power limits to create an efficient and intelligent next generation transportation system. Vehicular ad hoc network may now offer remarkable availability, reliability, and throughput using 6G technology. However, the VANET system’s data should be protected. This paper proposes an effective batch authentication and key exchange technique to avoid contact with hostile vehicle users. Moreover, three types of systems are proposed: PKI, ID-based, and MAC-based. The neuro-fuzzy inference technique was used to predict VANET security ratings. The Homogeneous Discrete-Time Markov Chain model is used to secure data transit. Additionally, this research examined the work from a blockchain perspective combined with MEC. There are 3 level to comprise the architecture: perception, edge computing, and services. Throughout the blockchain transmission process, the first layer make certain the security of VANET data. The perception layer makes use of edge computing and cloud services on the edge. The service layer protects data by using both traditional cloud storage and blockchain technology. The lowest layer of the system architecture is dedicated to the throughput and quality of service requirements of MEC users. The primary challenge is achieving consensus across blockchain nodes while maintaining the MEC system’s and blockchain’s performance. To simulate the joint optimization problem, a Markov decision process with reward function is utilized. The simulation results are conferred to illustrate the validity of study assertions.

Algeria is characterized by extreme cold in winter and high heat and humidity in summer. This leads to an increase in the use of electrical appliances, which has a negative impact on electrical energy consumption and its high costs, especially with the high price of electricity in Algeria. In this context, artificial intelligence can help to regulate the daily consumption of electricity, by optimizing the exploitation of natural resources and alerting the individual to avoid energy wasting. This paper proposes a decision-making tool (IRRHEM) for managing electrical energy at smart home. The IRRHEM solution is based on three elements: the use of natural resources, the notification of the inhabitants in case of resources misuse or wasting behavior, and the aggregation of similar activities at same time. Additionally, based on the proposed intelligent reasoning rules, residents’ behavior and activities are represented by OWL (Ontology Web Language) and written and executed through SWRL (Semantic Web Rule Language). Finally, the (IRRHEM) solution is tested in a home located in Algiers city inhabited by a family of four persons. The IRRHEM performance evaluation results are very promising and show a 3.60% rate of energy saving.

In the transmission of medical images, if the image is not processed, it is very likely to leak data and personal privacy, resulting in unpredictable consequences. Traditional encryption algorithms have limited ability to deal with complex data. The chaotic system is characterized by randomness and ergodicity, which has advantages over traditional encryption algorithms in image encryption processing. A novel V-net convolutional neural network (CNN) based on four-dimensional hyperchaotic system for medical image encryption is presented in this study. Firstly, the plaintext medical images are processed into 4D hyperchaotic sequence images, including image segmentation, chaotic system processing, and pseudorandom sequence generation. Then, V-net CNN is used to train chaotic sequences to eliminate the periodicity of chaotic sequences. Finally, the chaotic sequence image is diffused to change the raw image pixel to realize the encryption processing. Simulation test analysis demonstrates that the proposed algorithm has better effect, robustness, and plaintext sensitivity.

This paper provides an extensive and complete survey on the process of detecting and preventing various types of IoT-based security attacks. It is designed for software developers, researchers, and practitioners in the Internet of Things field who aim to understand the process of detecting and preventing these attacks. For each entry identified from the list, a brief description is provided along with references where more information can be found. However, We surveyed the current state-of-the-art IoT security solutions and focused on four main aspects: (1) handpicking representative attacks, (2) identifying potential solutions, (3) performing a threat analysis for each attack and solution, and (4) ranking solutions according to the threats they overcome. By adopting this framework, we identified five main categories of defense mechanisms: distributed denial of service detection/prevention, default password protection, encryption mechanisms, intrusion detection/prevention, and anomaly detection. These solutions are relatively mature in terms of utility and usability. However, the security analysis is conducted only concerning specific attacks, which may or may not be relevant to real-world deployment. Appropriate IoT security solutions should incorporate threat modeling while considering other factors such as resource consumption and implementation effort. Overall, evaluation of IoT security solutions is arduous due to the complexity of IoT OSes, heterogeneous IoT devices (e.g., various hardware platforms), limited availability of open-source codebases, and restrictive policies towards intellectual property disclosure. In addition, we note that there remains a lack of studies that perform a systematic evaluation of the state-of-the-art in terms of both frameworks/methodologies and mechanisms proposed.