This article proposes a software defined space-air-ground integrated network architecture for supporting diverse vehicular services in a seamless, efficient, and cost-effective manner. Firstly, the motivations and challenges for integration of space-air-ground networks are reviewed. Secondly, a software defined network architecture with a layered structure is presented. To protect the legacy services in satellite, aerial, and territorial segments, resources in each segment are sliced through network slicing to achieve service isolation. Then, available resources are put into a common and dynamic space-airground resource pool, which is managed by hierarchical controllers to accommodate vehicular services.Finally, a case study is carried out, followed by discussion on some open research topics. Index TermsSpace-air-ground integrated network, connected vehicles, software defined networking, network slicing. PLACE PHOTO HERE Shan Zhang received her Ph.D. degree in

In this paper, we consider a unified approach to model wireless channels by a mixture of Gaussian (MoG) distributions. The proposed approach provides an accurate approximation for the envelope and the signal-to-noise ratio (SNR) distributions of wireless channels. Simulation results have shown that our model can accurately characterize multipath fading and composite fading channels. We utilize the well known expectation-maximization algorithm to estimate the parameters of the MoG distribution and further utilize the Bayesian information criterion to determine the number of mixture components automatically. We employ the Kullback-Leibler divergence and the mean square error criteria to demonstrate that our distribution provides both high accuracy and low computational complexity. Additionally, we provide closed-form expressions or approximations for several performance metrics used in wireless communication systems, including the moment generating function, the raw moments, the amount of fading, the outage probability, the average channel capacity, and the probability of energy detection for cognitive radio. Numerical Analysis and Monte-Carlo simulation results are presented to corroborate the analytical results.

Botnets, which consist of remotely controlled compromised machines called bots, provide a distributed platform for several threats against cyber world entities and enterprises. Intrusion detection system (IDS) provides an efficient countermeasure against botnets. It continually monitors and analyzes network traffic for potential vulnerabilities and possible existence of active attacks. A payload-inspection-based IDS (PI-IDS) identifies active intrusion attempts by inspecting transmission control protocol and user datagram protocol packet's payload and comparing it with previously seen attacks signatures. However, the PI-IDS abilities to detect intrusions might be incapacitated by packet encryption. Traffic-based IDS (T-IDS) alleviates the shortcomings of PI-IDS, as it does not inspect packet payload; however, it analyzes packet header to identify intrusions. As the network's traffic grows rapidly, not only the detection-rate is critical, but also the efficiency and the scalability of IDS become more significant. In this paper, we propose a state-of-the-art T-IDS built on a novel randomized data partitioned learning model (RDPLM), relying on a compact network feature set and feature selection techniques, simplified subspacing and a multiple randomized meta-learning technique. The proposed model has achieved 99.984% accuracy and 21.38 s training time on a well-known benchmark botnet dataset. Experiment results demonstrate that the proposed methodology outperforms other well-known machine-learning models used in the same detection task, namely, sequential minimal optimization, deep neural network, C4.5, reduced error pruning tree, and randomTree.

In this paper, we investigate the performance of cooperative spectrum sensing (CSS) with multiple antenna nodes over composite and generalized fading channels. We model the probability density function (PDF) of the signal-to-noise ratio (SNR) using the mixture gamma (MG) distribution. We then derive a generalized closed-form expression for the probability of energy detection, which can be used efficiently for generalized multipath as well as composite (multipath and shadowing) fading channels. The composite effect of fading and shadowing scenarios in CSS is mitigated by applying an optimal fusion rule that minimizes the total error rate (TER), where the optimal number of nodes is

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