Moving target defense of routing randomization with deep reinforcement learning against eavesdropping attack

Eavesdropping attacks have become one of the most common attacks on networks because of their easy implementation. Eavesdropping attacks not only lead to transmission data leakage but also develop into other more harmful attacks. Routing randomization is a relevant research direction for moving target defense, which has been proven to be an effective method to resist eavesdropping attacks. To counter eavesdropping attacks, in this study, we analyzed the existing routing randomization methods and found that their security and usability need to be further improved. According to the characteristics of eavesdropping attacks, which are “latent and transferable”, a routing randomization defense method based on deep reinforcement learning is proposed. The proposed method realizes routing randomization on packet-level granularity using programmable switches. To improve the security and quality of service of legitimate services in networks, we use the deep deterministic policy gradient to generate random routing schemes with support from powerful network state awareness. In-band network telemetry provides real-time, accurate, and comprehensive network state awareness for the proposed method. Various experiments show that compared with other typical routing randomization defense methods, the proposed method has obvious advantages in security and usability against eavesdropping attacks.

Individual repeatability in timing and spatial flexibility of migration routes of trans-Saharan migratory raptors

Satellite-tracking technology has allowed scientists to make a quantum leap in the field of migration ecology. Nowadays, the basic description of migratory routes of many species of birds has been reported. However, the investigation of bird migration at individual level （i.e. repeatability in migratory routes and timing） still remains seldom explored. Here, we investigated repeated migratory trips of a trans-Saharan endangered migratory raptor, the Egyptian Vulture Neophron percnopterus, tracked by GPS satellite telemetry. We compared between- and within-individual variation in migratory routes and timing in order to assess the degree of repeatability （or conversely, the flexibility） in migration. To this end, we analysed a dataset of 48 trips （23 springs and 25 autumns） recorded for six adult birds during 2007-2013. Our results showed consistent migration timing at the individual level, both in spring and autumn. Interestingly, there was a high degree of flexibility in the routes followed by the same individual in different years, probably due to variations in meteorological conditions. Contrary to expectations of a faster migration in spring than in autumn owing to a time-minimization strategy for breeding, birds spent less time in autumn migration （13 ± 2 days, range = 9-18 d） than in spring migration （19 ± 3 days, range = 13-26 d）, which can be explained by differences in environmental con- ditions en route. Egyptian vultures showed a consistent clockwise loop migration through western Africa, following more easterly routes in autumn than in spring. Finally, our results provide supporting evidence of low phenotypic plasticity in timing of migration （i.e. strong endogenous control of migration） and high flexibility in routes [Current Zoology 60 （5）： 642-652, 2014].

Data-flow in mobile edge computing networks: end-to-end performance analysis using stochastic network calculus

Mobile edge computing(MEC) networks can provide a variety of services for different applications. End-to-end performance analysis of these services serves as a benchmark for the efficient planning of network resource allocation and routing strategies. In this paper, a performance analysis framework is proposed for the end-to-end data-flows in MEC networks based on stochastic network calculus(SNC). Due to the random nature of routing in MEC networks, probability parameters are introduced in the proposed analysis model to characterize this randomness into the derived expressions. Taking actual communication scenarios into consideration, the end-to-end performance of three network data-flows is analyzed, namely, voice over Internet protocol(VoIP), video, and file transfer protocol(FTP). These network data-flows adopt the preemptive priority scheduling scheme. Based on the arrival processes of these three data-flows, the effect of interference on their performances and the service capacity of each node in the MEC networks, closed-form expressions are derived for showing the relationship between delay, backlog upper bounds, and violation probability of the data-flows. Analytical and simulation results show that delay and backlog performances of the data-flows are influenced by the number of hops in the network and the random probability parameters of interference-flow(IF).