No abstract

Abstract-In this paper, we propose an efficient pseudonymous authentication scheme with strong privacy preservation, named PASS, for vehicular communications. Unlike traditional pseudonymous authentication schemes, the size of Certificate Revocation List (CRL) in PASS is linear with the number of revoked vehicles and irrelated to how many pseudonymous certificates are held by the revoked vehicles. PASS supports Roadside Unitsaided distributed certificate service that allows the vehicles to update certificates on road, but the service overhead is almost irrelated to the number of the updated certificates. Furthermore, PASS provides strong privacy preservation to the vehicles so that the adversaries can not trace any vehicle even all Roadside Units have been compromised. Extensive simulations demonstrate that PASS outperforms previously reported ones in terms of the revocation cost and the certificate updating overhead.

No abstract

No abstract

The main task of future networks is to build, as much as possible, intelligent networking architectures for intellectualization, activation, and customization. Software-defined networking (SDN) technology breaks the tight coupling between the control plane and the data plane in the traditional network architecture, making the controllability, security, and economy of network resources into a reality. As one of the important actualization methods of artificial intelligence (AI), machine learning (ML), combined with SDN architecture will have great potential in areas, such as network resource management, route planning, traffic scheduling, fault diagnosis, and network security. This paper presents the network applications combined with SDN concepts based on ML from two perspectives, namely the perspective of ML algorithms and SDN network applications. From the perspective of ML algorithms, this paper focuses on the applications of classical ML algorithms in SDN-based networks, after a characteristic analysis of algorithms. From the other perspective, after classifying the existing network applications based on the SDN architecture, the related ML solutions are introduced. Finally, the future development of the ML algorithms and SDN concepts is discussed and analyzed. This paper occupies the intersection of the AI, big data, computer networking, and other disciplines; the AI itself is a new and complex interdisciplinary field, which causes the researchers in this field to often have different professional backgrounds and, sometimes, divergent research purposes. This paper is necessary and helpful for researchers from different fields to accurately master the key issues.INDEX TERMS Artificial intelligence, machine learning, network management, software-defined networking.