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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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Wireless Sensor Networks (WSNs) are becoming more popular for many applications due to their convenient services. However, sensor nodes may suffer from significant security flaws, leading researchers to propose authentication schemes to protect WSNs. Although these authentication protocols significantly fulfill the required protection, security enhancement with less energy consumption is essential to preserve the availability of resources and secure better performance. In 2020, Youssef et al. suggested a scheme called Enhanced Probabilistic Cluster Head Selection (LEACH-PRO) to extend the sensors' lifetime in WSNs. This paper introduces a new variant of the LEACH-PRO protocol by adopting the blockchain security technique to protect WSNs. The proposed protocol (SLEACH-PRO) performs a decentralized authentication mechanism by applying a blockchain to multiple base stations to avoid system and performance degradation in the event of a station failure. The security analysis of the SLEACH-PRO is performed using Burrows–Abadi–Needham (BAN) logic and Automated Validation of Internet Security Protocols and Applications (AVISPA) tool. Moreover, the SLEACH-PRO is evaluated and compared to related protocols in terms of computational cost and security level based on its resistance against several attacks. The comparison results showed that the SLEACH-PRO protocol is more secure and requires less computational cost compared to other related protocols. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Issues such as maintaining the security and integrity of data in digital healthcare are growing day-by-day in terms of size and cost. The healthcare industry needs to work on effective mechanisms to manage these concerns and prevent any debilitating crisis that might affect patients as well as the overall health management. To tackle such critical issues in a simple, feasible, and symmetrical manner, the authors considered the ideology of cybernetics. Working towards this intent, this paper proposes a symmetrical model that illustrates a compact version of the adopted ideology as a pathway for future researchers. Furthermore, the proposed ideology of cybernetics specifically focuses on how to plan the entire design concept more effectively. It is important for the designer to prepare for the future and manage the design structure from a product perspective. Therefore, the proposed ideology provides a symmetric mechanism that includes a variety of estimation and evaluation techniques as well as their management. The proposed model generates a symmetric, variety-issue, reduced infrastructure that can produce highly effective results due to an efficient usability, operatability, and symmetric operation execution which are the benefits of the proposed model. Furthermore, the study also performed a performance simulation assessment by adopting a multi-criteria decision-making approach that helped the authors compare the various existing and proposed models based on their levels of effectiveness. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Most plant viral infections are vector-borne. There is a latent period of disease inside the vector after obtaining the virus from the infected plant. Thus, after interacting with an infected vector, the plant demonstrates an incubation time before becoming diseased. This paper analyzes a mathematical model for persistent vector-borne viral plant disease dynamics. The backpropagated neural network based on the Levenberg—Marquardt algorithm (NN-BLMA) is used to study approximate solutions for fluctuations in natural plant mortality and vector mortality rates. A state-of-the-art numerical technique is utilized to generate reference data for obtaining surrogate solutions for multiple cases through NN-BLMA. Curve fitting, regression analysis, error histograms, and convergence analysis are used to assess accuracy of the calculated solutions. It is evident from our simulations that NN-BLMA is accurate and reliable. [ABSTRACT FROM AUTHOR]

It is generally observed that aquatic organisms have symmetric abilities to produce oxygen (O 2) and fix carbon dioxide (C O 2) . A simulation model with time-dependent parameters was recently proposed to better understand the symmetric effects of accelerated climate change on coastal ecosystems. Changes in environmental elements and marine life are two examples of variables that are expected to change over time symmetrically. The sustainability of each equilibrium point is examined in addition to proving the existence and accuracy of the proposed model. To support the conclusions of this research compared to other studies, numerical simulations of the proposed model and a case study are investigated. This paper proposes an integrated bibliographical analysis of artificial neural networks (ANNs) using the Reverse-Propagation with Levenberg–Marquaradt Scheme (RP-LMS) to evaluate the main properties and applications of ANNs. The results obtained by RP-LMS show how to prevent global warming by improving the management of marine fish resources. The reference dataset for greenhouse gas emissions, environmental temperature, aquatic population, and fisheries population (GAPF) is obtained by varying parameters in the numerical Adam approach for different scenarios. The accuracy of the proposed RP-LMS neural network is demonstrated using mean square error (MSE), regression plots, and best-fit output. According to RP-LMS, the current scenario of rapid global warming will continue unabated over the next 50 years, damaging marine ecosystems, particularly fish stocks. [ABSTRACT FROM AUTHOR]

The present study concerns the modeling of the thermal behavior of a porous longitudinal fin under fully wetted conditions with linear, quadratic, and exponential thermal conductivities surrounded by environments that are convective, conductive, and radiative. Porous fins are widely used in various engineering and everyday life applications. The Darcy model was used to formulate the governing non-linear singular differential equation for the heat transfer phenomenon in the fin. The universal approximation power of multilayer perceptron artificial neural networks (ANN) was applied to establish a model of approximate solutions for the singular non-linear boundary value problem. The optimization strategy of a sports-inspired meta-heuristic paradigm, the Tiki-Taka algorithm (TTA) with sequential quadratic programming (SQP), was utilized to determine the thermal performance and the effective use of fins for diverse values of physical parameters, such as parameter for the moist porous medium, dimensionless ambient temperature, radiation coefficient, power index, in-homogeneity index, convection coefficient, and dimensionless temperature. The results of the designed ANN-TTA-SQP algorithm were validated by comparison with state-of-the-art techniques, including the whale optimization algorithm (WOA), cuckoo search algorithm (CSA), grey wolf optimization (GWO) algorithm, particle swarm optimization (PSO) algorithm, and machine learning algorithms. The percentage of absolute errors and the mean square error in the solutions of the proposed technique were found to lie between 10 − 4 to 10 − 5 and 10 − 8 to 10 − 10 , respectively. A comprehensive study of graphs, statistics of the solutions, and errors demonstrated that the proposed scheme's results were accurate, stable, and reliable. It was concluded that the pace at which heat is transferred from the surface of the fin to the surrounding environment increases in proportion to the degree to which the wet porosity parameter is increased. At the same time, inverse behavior was observed for increase in the power index. The results obtained may support the structural design of thermally effective cooling methods for various electronic consumer devices. [ABSTRACT FROM AUTHOR]

Implementing security and privacy requirements at every level of the software development cycle is imperative for ensuring optimum usability as well as the users' satisfaction. Software development must consider and comply effectively with the risks involved in the privacy and protection of confidential data. This research study endeavors to integrate the standards of data protection along with the Security Threat Oriented Requirements Engineering (STORE) methodology in order to recognize the potential threats to privacy requirements. The proposed extension of the STORE methodology, called the P-STORE, is validated by a case study of the Healthcare Management Software (HMS) system project. Furthermore, we have used the integrated fuzzy AHP with fuzzy TOPSIS technique for the usability assessment of different privacy requirements engineering approaches including the P-STORE methodology. The study demonstrates that the P-STORE approach has the capability to elicit more efficient privacy requirements and that it allows the software engineer to arrange privacy requirements efficaciously. [ABSTRACT FROM AUTHOR]

Predicting student performance for educational organizations such as universities, community colleges, schools, and training centers will enhance the overall results of these organizations. Big data can be extracted from the internal systems of these organizations, such as exam records, statistics about virtual courses, and e-learning systems. Finding meaningful knowledge from extracted data is a challenging task. In this paper, we proposed a modified version of Harris Hawks Optimization (HHO) algorithm by controlling the population diversity to overcome the early convergence problem and prevent trapping in a local optimum. The proposed approach is employed as a feature selection algorithm to discover the most valuable features for student performance prediction problem. A dynamic controller that controls the population diversity by observing the performance of HHO using the k-nearest neighbors (kNN) algorithm as a clustering approach. Once all solutions belong to one cluster, an injection process is employed to redistribute the solutions over the search space. A set of machine learning classifiers such as kNN, Layered recurrent neural network (LRNN), Naïve Bayes, and Artificial Neural Network are used to evaluate the overall prediction system. A real dataset obtained from UCI machine learning repository is adopted in this paper. The obtained results show the importance of predicting students' performance at an earlier stage to avoid students' failure and improve the overall performance of the educational organization. Moreover, the reported results show that the combination between the enhanced HHO and LRNN can outperform other classifiers with accuracy equal to 92 % , since LRNN is a deep learning algorithm that is able to learn from previous and current input values. [ABSTRACT FROM AUTHOR]

• This research emphasizes enhancing data security in real-time IoV communication considering secure communication also presents a trust-driven privacy method using encryption and steganography for IoV, "Efficient Algorithm for Secure Transmission (EAST)," integrating encryption and steganography algorithms. • We develop a data encryption method for IoV communication and verification using distinct symmetric encryption for all three components of vehicle communication. • Development of the standards on multiple systems for secure data transmission in IoV. Comprehensive performance measurement from various angles, including transaction cost, memory overhead, processing time, and energy use. • The proposed EAST method is compared with existing AES [12] , DES [13] , G-DES [14] , and standard LSB [15] using different indicators such as time efficiency, avalanche effect, PSNR, and file cover size. High-speed wireless network technologies play a vital role in autonomous vehicle communication systems (AVS), i.e., IoV (Internet of Vehicles), and enhance the effectiveness and reliability of the communication network. Since the size of IoV communication is rapidly growing, it is now an essential part of our regular routines. Due to its dynamic nature and massive size, it is essential to avoid any miss-communication and protect the data privacy of each vehicle.IoVallowsAVS to link remotely to each other and to various platforms and groups that may be using the same communications system. This quality link between vehicles and the need for information exchanges among vehicles introduces vulnerability and threats that can be exploited by cybercriminals. This research emphasizes on enhancing data security in real-time IoV communication. It presents a trust-driven privacy method using encryption and steganography for IoV. This method uses an Efficient Algorithm for Secure Transmission (EAST) that integrates encryption and steganography algorithms. The proposed EAST is then compared to the various current state of arts, i.e., AES (Advanced Encryption Standard), DES (Data Encryption Standard), G-DES (Generalized DES), and Standard LSB on the basis of various performance measuring parameters, i.e., encryption and decryption time, efficiency, avalanche effect, PSNR, and file cover size. Experimental results are promising for the proposed EAST method, which reported a better time efficiency of 0.86 ms, avalanche effect of 58.81%, and PSNR of 78.58%, compared to the current state arts. [Display omitted] [ABSTRACT FROM AUTHOR]

The new generation of wireless communication systems has adopted different waveforms. The universal filtered multicarrier is one of the adopted candidates that has symmetry with various numerology designs. However, the high peak to average power ratio is one of the major limitations faced by universal filter multicarrier (UFMC) designers. Moreover, recent studies utilize cubic metric along with the peak to average power ratio (PAPR) to show the power back-off effect of the signal in which the PAPR metric identifies the maximum peak and the cubic metric (CM) identifies the Out of Band emission and In-Band distortion. Most of the current solutions, such as amplitude clipping, tone reservation, and active constellation extension, decrease the PAPR but cause degradation to the bit error rate. Selected mapping is one of the promising techniques that is recently used to solve the PAPR and CM problems without causing bit error rate (BER) degradation. In this paper, the selected mapping (SLM) is integrated with UFMC to reduce the PAPR and CM without affecting the BER of 5G networks. The SLM-UFMC solution model is simulated by MATLAB and the results show that the SLM-UFMC model presents better PAPR and CM performance without BER degradation. The PAPR has been decreased to 1.5 dB with respect to eight-phase rotation vectors and the CM decreased to 1.25 dB compared to the conventional UFMC. [ABSTRACT FROM AUTHOR]

Growing concern about healthcare information security in the wake of alarmingly rising cyber-attacks is being given symmetrical priority by current researchers and cyber security experts. Intruders are penetrating symmetrical mechanisms of healthcare information security continuously. In the same league, the paper presents an overview on the current situation of healthcare information and presents a layered model of healthcare information management in organizations. The paper also evaluates the various factors that have a key contribution in healthcare information security breaches through a hybrid fuzzy-based symmetrical methodology of AHP-TOPSIS. Furthermore, for assessing the effect of the calculated results, the authors have tested the results on local hospital software of Varanasi. Tested results of the factors are validated through the comparison and sensitivity analysis in this study. Tabulated results of the proposed study propose a symmetrical mechanism as the most conversant technique which can be employed by the experts and researchers for preparing security guidelines and strategies. [ABSTRACT FROM AUTHOR]

Increasing the number of threats against software vulnerabilities and rapidly growing data breaches have become a key concern for both the IT industry and stakeholders. Developing secure software systems when there is a high demand for software products from individuals as well as the organizations is in itself a big challenge for the designers and developers. Meanwhile, adopting traditional and informal learnings to address security issues of software products has made it easier for cyber-criminals to expose software vulnerabilities. Hence, it is imperative for the security practitioners to employ a symmetric mechanism so as to achieve the desired level of software security. In this context, a decision-making approach is the most symmetrical technique to assess the security of software in security tactics perspective. Since the security tactics directly address the quality attribute concerns, this symmetric approach will be highly effective in making the software systems more secure. In this study, the authors have selected three main attributes and fifteen sub-attributes at level 1 and level 2, respectively, with ten different software of an institute as alternatives. Furthermore, this study uses a fuzzy-based symmetrical decision-making approach to assess the security of software with respect to tactics. Fuzzy Analytic Network Process (F-ANP) is applied to evaluate the weights of criteria and fuzzy-Symmetrical technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is used to determine impact of alternatives. The proposed symmetrical assessment in this study will be beneficial for both the designers and developers to categorize and prioritize the security attributes and understand the importance of security tactics during software development life cycle. [ABSTRACT FROM AUTHOR]