With the wide application and deployment of cloud computing in enterprises, virtualization developers and security researchers are paying more attention to cloud computing security. The core component of cloud computing products is the hypervisor, which is also known as the virtual machine monitor (VMM) that can isolate multiple virtual machines in one host machine. However, compromising the hypervisor can lead to virtual machine escape and the elevation of privilege, allowing attackers to gain the permission of code execution in the host. Therefore, the security analysis and vulnerability detection of the hypervisor are critical for cloud computing enterprises. Importantly, virtual devices expose many interfaces to a guest user for communication, making virtual devices the most vulnerable part of a hypervisor. However, applying fuzzing to the virtual devices of a hypervisor is challenging because the data structures transferred by DMA are constructed in a nested form according to protocol specifications. Failure to understand the protocol of the virtual devices will make the fuzzing process stuck in the initial fuzzing stage, resulting in inefficient fuzzing.

In this paper, a framework for designing transmit diversity schemes for LTE PUCCH format 1b with channel selection is introduced. The design criterion is to utilize the same number of orthogonal resources as in the single transmit antenna mode while exploiting the full diversity order. It is shown that the code design is decoupled into resource coding and modulation coding. A sample code is presented and its performance is compared with single antenna transmission.

An algorithm for direction finding and array calibration based on the maximal set of nonredundant cumulants (MSNC) is presented in this paper. The algorithm utilizes all nonredundant information of the spatial fourth-order cumulants. It has been shown that the algorithm is insensitive to additive Gaussian noise and superior to conventional covariance-based DOA algorithms with reduced estimation variances and enhanced resolvability. The algorithm also circumvents the iteration between direction finding and array calibration, without the problem of convergence and with improved robustness. Simulation experiments demonstrate the potential performance of the proposed algorithm.