Low earth orbit satellite communication has lower latency and is more suitable for real time communication than high orbit and medium orbit satellites, secure networking is crucial for providing continuous services to low earth orbit satellites. This paper proposes a method for dynamic secure networking of low earth orbit satellites, the method is based on signcryption elliptic curve algorithm in ISO/IEC 29150:2011. The biggest contribution is the combination of certificateless public key mechanism and signcryption algorithm to support secure networking of satellites. Finally, Scyther was used to conduct security analysis on the algorithm and process, it has been proven that the certificateless public key signcryption system can effectively solve the problems of authentication and secure transmission between satellites.

It is well known that the future network will achieve seamless global coverage, and the convergence of satellite and terrestrial networks is the basic feature of the next generation network evolution(6G) in a complementary way that satellite network has global coverage and disaster resistance while terrestrial network has large-scale base stations and perfect coverage capability. Meanwhile, the openness of satellite wireless channels, the time variability of satellite networking, and the heterogeneity of the converged network makes the converged network face unprecedented security challenges. This paper analyzes the new security risks introduced due to the inherent converged network features, designs a promising method which is named NAKA for satellite terminal access satellite network to facilitate authentication unification, and wireless security negotiation protection process to solve the authentication and security initialization in the access process from a security perspective.

Summary An effective strategy for accelerating the calculation of convex hulls is to filter the input points by discarding interior points. In this paper, we present such a straightforward preprocessing approach by discarding the points locating in a convex polygon formed by 16 extreme points. Extreme points of a planar point set do not alter when all points are rotated with the same angle in the plane. Four groups of four extreme points with min or max x or y coordinates can be found for the original point set and three rotated point sets. These 16 extreme points are used to form a planar convex polygon. We discard those points locating in the convex polygon and calculate the desired convex hull of the remaining points. The proposed preprocessing algorithm is evaluated on two computational platforms. Experiments show that, when employing the proposed preprocessing algorithm on the computational platform 1, it achieves speedups of approximately 4 ×∼5× on average and 5 ×∼6× in the best cases over the cases where the proposed approach is not used, while on the computational platform 2, the speedups are approximately 6 ×∼9× on average and 9 ×∼14× in the best cases. Moreover, more than 99% input points can be discarded in most cases.

An effective strategy for accelerating the calculation of convex hulls for planar point sets is to filter the input points by discarding interior points. In this paper, we present such a straightforward preprocessing approach by exploiting the modern Graphics Processing Unit. The basic idea behind our approach is to discard the points that locate inside a convex polygon formed by 16 extreme points. Due to the fact that the extreme points of a planar point set do not alter when all points are rotated with the same angle in the plane, four groups of extreme points with min or max x or y coordinates can be found in the original point set and three rotated point sets. These 16 extreme points are then used to form a planar convex polygon. We check all input points and discard the points that locate inside the convex polygon, and then use the remaining points to calculate the desired convex hull. Experimental results show that: when employing the proposed preprocessing algorithm, it achieves the speedups of about 4x ~ 5x on average and 5x ~ 6x in the best cases over the cases where the proposed approach is not used. In addition, more than 99% input points can be discarded in most experimental tests.