One of the most significant threats that hinder the effective functioning of existing models and technologies in 6G networks, particularly within the realms of Industry 5.0, is the lack of privacy and security. Traditional cryptographic models that were once considered secure have proven to be vulnerable to an expanding range of threats, such as quantum attacks. This vulnerability results in the exposure of sensitive data to unauthorized breaches and compromises the integrity and overall functionality of the network. Access control mechanisms implemented up to this point could not ensure that security compromises are prevented as secure data sharing is facilitated effortlessly. As a result, attempts to access sensitive data without authorization are often made, leaving the networks vulnerable to DDoS attacks that have the potential to disrupt their operation. Considering these pervasive challenges, our paper proposes an innovative hybrid protocol suite explicitly designed for 6G networks in Industry 5.0. Specifically, our approach integrates leading-edge technologies, such as Nth-degree Truncated polynomial Ring Unit Encryption with Supersingular Isogeny Diffie–Hellman (NTRUEncrypt-SIDH). and Key Policy attribute-based encryption (KP-ABE). It aims to address the vulnerabilities associated with traditional cryptographic models to ensure high confidentiality and authorized data sharing. Moreover, we implement modern DDoS mitigation techniques, such as rate limiting, traffic filtering, and adaptive routing, to enhance the resilience of the network and ensure that system operation remains uninterrupted. Experimental evaluations demonstrate the superiority of the proposed model over existing standards. Notably, the hybrid protocol achieves encryption and decryption times of 1.8 seconds and 1.667 seconds per GB, respectively, with a key generation time of 2.857 seconds per GB, ensuring efficient performance even in resource-constrained environments. The system achieves a total computational overhead of 11.7 milliseconds for KP-ABE combined with NTRUEncrypt-SIDH, outperforming other cryptographic combinations. Additionally, the model exhibits enhanced scalability, flexibility, and security, offering robust defenses against quantum and classical threats while ensuring seamless user policy-based access control. These results underscore the proposed hybrid cryptosystem's potential as a transformative standard for the secure and efficient operation of 6G networks in Industry 5.0.

Public transportation is a vital service provided to enable a community to carry out daily activities. One of the mass transportations used in an area is a bus. Moreover, the smart transportation concept is an integrated application of technology and strategy in the transportation system. Using smart idea is the key to the application of the Internet of Things. The ways to improve the management transportation system become a bottleneck for the traditional data analytics solution, one of the answers used in machine learning. This paper uses the Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithm for the best prediction of travel time with a lower error rate on a case study of a university shuttle bus. Apart from predicting the travel time, this study also considers the fuel cost and gas emission from transportation. The analysis of the experiment shows that the ANN outperformed the SVM. Furthermore, a recommender system is used to recommend suitable routes for the chosen scenario. The experiments extend the discussion with a range of future directions on the stipulated field of study.

Vehicular Ad Hoc Networks (VANETs) are rapidly gaining attention due to the diversity of services that they can potentially offer. However, VANET communication is vulnerable to numerous security threats such as Distributed Denial of Service (DDoS) attacks. Dealing with these attacks in VANET is a challenging problem. Most of the existing DDoS detection techniques suffer from poor accuracy and high computational overhead. To cope with these problems, we present a novel Multivariant Stream Analysis (MVSA) approach. The proposed MVSA approach maintains the multiple stages for detection DDoS attack in network. The Multivariant Stream Analysis gives unique result based on the Vehicle‐to‐Vehicle communication through Road Side Unit. The approach observes the traffic in different situations and time frames and maintains different rules for various traffic classes in various time windows. The performance of the MVSA is evaluated using an NS2 simulator. Simulation results demonstrate the effectiveness and efficiency of the MVSA regarding detection accuracy and reducing the impact on VANET communication.