Summary Network virtualization is a promising technology, which is used to solve the existing shortcomings of the Internet architecture and in which virtual network (VN) embedding plays a vital role in resource allocation. However, current virtual network embedding algorithms mainly deal with static VN embedding; the allocations of network resources are constant and excessive in the VN requests' lifetime. In reality, the user's resource requirements usually change with time; hence, static VN embedding algorithms can lead to low utilization of substrate resources and reduction of the operators' revenues. To solve the aforementioned problem, we focus on the virtual network embedding considering dynamic demands in this paper. We apply supervised radial basis function (RBF) neural network to predict the changes of virtual requests and dynamically reallocate substrate resources by adjusting already allocated resources. Simulation experiments show that compared with the static algorithms, dynamic VN embedding schemes with supervised RBF can more accurately predict the demand changes of virtual networks, which adaptively adjusts resources allocation and management and therefore improves resource utilization of substrate networks.

Controller Area Network (CAN), the de facto standard in-vehicle network protocol, prompts modern automobile an integrated system that achieves real-time interactions with roads, vehicles and people. Yet such connectivity makes it feasible to illegally access, or even attack the CAN, causing not only privacy disclosure, property damage, but also life threat. In this paper, we analyze intrinsic weakness in CAN protocol that is mostly exploited by attackers and comprehensively survey the existing attacks based on CAN interfaces. Furthermore, we propose an attack evaluation system based on attack tree model and Markov chain to assess the probability of compromising CAN and the steady state of CAN system at the presence of these attacks. Finally, we simulate new steady state when altering the difficulty of a certain attack and the results demonstrate that sometimes improving defense of an attack declines the security level of the entire system instead.

With rapid growth of LTE network and Voice-over-LTE(VoLTE), detecting and preventing security threats like Denial of Service attack becomes a necessary and urgent requirement. VoLTE is an voice solution based on Internet Protocol and 4G LTE technology, at the same time exposing many vulnerabilities when using packet-switched network. There are many heavy weighted detection systems using content analysis, while high demands of computing resource constraint their practical use. In this paper, we purpose a lightweight detection scheme for VoLTE network security, based on analysis of data traffic flow. To optimize parameters in our scheme, we formulate a Bayesian game model. Bayesian game has the features of incomplete information and asymmetry, similar to practical attack-defense model. Besides, The dynamic Bayesian game is more realistic, since both sides can update believes about their opponents. Simulation results provide some guidances on parameter selection, as well as verifying the superiority of our scheme.

For the first time, one new conception, multi-proxy multi-signature binding positioning protocol, is proposed. Based on the secure positioning protocol, one model of multi-proxy multi-signature binding positioning protocol is proposed. In the model, positioning protocol is bound to multi-proxy multi-signature tightly, not loosely. Further, we propose one scheme of multi-proxy multi-signature binding positioning protocol, its correctness is proved, and its security is analyzed. Copyright © 2016 John Wiley & Sons, Ltd.

Nowadays, most proxy signature schemes are based on the difficulty of DLP (Discrete Logarithm Problem) or ECDLP (Elliptical Curve Discrete Logarithm Problem). As though many proxy signature schemes based on DLP or ECDLP have been proposed, it makes us discouraged that some disadvantages can be found