The security and privacy concerns in vehicular communication are often faced with schemes depending on either elliptic curve (EC) or bilinear pair (BP) cryptographies. However, the operations used by BP and EC are time-consuming and more complicated. None of the previous studies fittingly tackled the efficient performance of signing messages and verifying signatures. Therefore, a chaotic map-based conditional privacy-preserving authentication (CM-CPPA) scheme is proposed to provide communication security in 5G-enabled vehicular networks in this paper. The proposed CM-CPPA scheme employs a Chebyshev polynomial mapping operation and a hash function based on a chaotic map to sign and verify messages. Furthermore, by using the AVISPA simulator for security analysis, the results of the proposed CM-CPPA scheme are good and safe against general attacks. Since EC and BP operations do not employ the proposed CM-CPPA scheme, their performance evaluation in terms of overhead such as computation and communication outperforms other most recent related schemes. Ultimately, the proposed CM-CPPA scheme decreases the overhead of computation of verifying the signatures and signing the messages by 24.2% and 62.52%, respectively. Whilst, the proposed CM-CPPA scheme decreases the overhead of communication of the format tuple by 57.69%.

Aim/Purpose: Effective e-learning systems need to incorporate student characteristics such as learning style and knowledge level in order to provide a more personalized and adaptive learning experience. However, there is a need to investigate how and when to provide adaptivity based on student characteristics, and more importantly, to evaluate its value in learning enhancement. This study aims to bridge that gap by examining the effect of different modes of learning material adaptation and their sequences to the learning style and knowledge level of students in e-learning systems. Background: E-learning systems aim to provide acceptability and interactivity between students, instructors, and learning content anytime and anywhere. However, traditional systems are typically designed for generic students irrespective of individual requirements. Successful e-learning systems usually consider student characteristics such as learning style and knowledge level to provide more personalized and adaptive student-system interaction. Methodology: A controlled experiment was conducted in a learning context with 174 subjects to evaluate the learning effectiveness of adaptivity in e-learning systems. Contribution: The main contributions of the paper are threefold. First, a novel adaptive approach is proposed based on a specific learning style model and knowledge level. Second, the approach is implemented in an e-learning system to teach computer security, the application domain. Third, a rigorous experimental evaluation of the learning effect of the adaptive approach is offered. Findings: The results indicate that adaptation according to the combination of learning style and knowledge level produces significantly better learning gains, both in the short-term and medium-term, than adaptation according to either trait individually. Recommendations for Practitioners: Practitioners should consider the combination of learning style and knowledge level when delivering and presenting learning material to their students. Recommendation for Researchers: Researchers should consider sound educational models when designing adaptive e-learning systems. Also, rigorous and carful experimental design evaluations should be taken into account. Impact on Society: Universities and e-learning industries can benefit from the proposed adaptive approach and the findings in designing and developing more personalized and adaptive e-learning systems. The incorporation of student characteristics, especially learning style and knowledge level, may be used to enhance learning. Future Research: The experiment might be duplicated with a focus on longer-term learning gains by including more subjects and more learning resources. Also, the study might be expanded to application domains other than computer security. Moreover, other variables such as student satisfaction, motivation, and affective state might be explored to further the understanding of the effect of adaptivity on learning gains.

Both security and privacy are central issues and need to be properly handled because communications are shared among vehicles in open channel environments of 5G-enabled vehicular networks. Several researchers have proposed authentication schemes to address these issues. Nevertheless, these schemes are not only vulnerable to quantum attacks but also use heavy operations to generate and verify signatures of messages. Additionally, these schemes need an expensive component RoadSide Unit (RSU)-aided scheme during the joining phase. To address these issues, we propose a lightweight quantum-resistant scheme according to the lattice method in 5G-enabled vehicular networks. Our proposal uses matrix multiplication instead of operations-based bilinear pair cryptography or operations-based elliptic curve cryptography to generate and verify signatures of messages shared among vehicles. Our proposal satisfies a significant reduction in performance, which makes it lightweight enough to handle quantum attacks. Our proposal is based on 5G technology without using any RSU-aided scheme. Security analysis showed that our proposal satisfies privacy and security properties as well as resists quantum attacks. Finally, our proposal also shows favorable performance compared to other related work.

The vehicles in the fifth-generation (5G)-enabled vehicular networks exchange the data about road conditions, since the message transmission rate and the downloading service rate have been considerably brighter. The data shared by vehicles are vulnerable to privacy and security issues. Notably, the existing schemes require expensive components, namely a road-side unit (RSU), to authenticate the messages for the joining process. To cope with these issues, this paper proposes a provably secure efficient data-sharing scheme without RSU for 5G-enabled vehicular networks. Our work included six phases, namely: TA initialization (TASetup) phase, pseudonym-identity generation (PIDGen) phase, key generation (KeyGen) phase, message signing (MsgSign) phase, single verification (SigVerify) phase, and batch signatures verification (BSigVerify) phase. The vehicle in our work has the ability to verify multiple signatures simultaneously. Our work not only achieves privacy and security requirements but also withstands various security attacks on the vehicular network. Ultimately, our work also evaluates favourable performance compared to other existing schemes with regards to costs of communication and computation.

The privacy and security vulnerabilities in fifth-generation (5G)-enabled vehicular networks are often required to cope with schemes based on either bilinear pair cryptography (BPC) or elliptic curve cryptography (ECC). Nevertheless, these schemes suffer from massively inefficient performance related to signing and verifying messages in areas of the high-density traffic stream. Meanwhile, adversaries could launch side-channel attacks to obtain sensitive data protected in a tamper-proof device (TPD) to destroy the system. This paper proposes a Chebyshev polynomial-based scheme for resisting side-channel attacks in 5G-enabled vehicular networks. Our work could achieve both important properties of the Chebyshev polynomial in terms of chaotic and semi-group. Our work consists of five phases: system initialization, enrollment, signing, verification, and pseudonym renew. Moreover, to resist side-channel attacks, our work renews periodically and frequently the vehicle’s information in the TPD. Security analysis shows that our work archives the privacy (pseudonym identity and unlikability) and security (authentication, integrity, and traceability) in 5G-enabled vehicular networks. Finally, our work does not employ the BPC or the ECC; its efficiency performance outperforms other existing recent works, making it suitable for use in vehicular networks.

The rapid expansion of medical data poses numerous challenges for Machine Learning (ML) tasks due to their potential to include excessive noisy, irrelevant, and redundant features. As a result, it is critical to pick the most pertinent features for the classification task, which is referred to as Feature Selection (FS). Among the FS approaches, wrapper methods are designed to select the most appropriate subset of features. In this study, two intelligent wrapper FS approaches are implemented using a new meta-heuristic algorithm called Sand Cat Swarm Optimizer (SCSO). First, the binary version of SCSO, known as BSCSO, is constructed by utilizing the S-shaped transform function to effectively manage the binary nature in the FS domain. However, the BSCSO suffers from a poor search strategy because it has no internal memory to maintain the best location. Thus, it will converge very quickly to the local optimum. Therefore, the second proposed FS method is devoted to formulating an enhanced BSCSO called Binary Memory-based SCSO (BMSCSO). It has integrated a memory-based strategy into the position updating process of the SCSO to exploit and further preserve the best solutions. Twenty one benchmark disease datasets were used to implement and evaluate the two improved FS methods, BSCSO and BMSCSO. As per the results, BMSCSO acted better than BSCSO in terms of fitness values, accuracy, and number of selected features. Based on the obtained results, BMSCSO as a FS method can efficiently explore the feature domain for the optimal feature set.