Cloud computing nowadays provides flexible and scalable computing services, using different hardware platforms, including ARM. Virtualization allows multiple virtual machines (VMs) to share the physical resources of a host machine. However, these technologies have security risks. The hypervisor is the software that controls VMs, and it can be exploited or manipulated by hackers or untrusted providers. ARM CCA, a novel feature of ARMv9-A, allows confidential VMs to run in a new security state called realm. However, the current CCA prototype still has some problems, including risks brought by external libraries, single point of failure, highly privileged TF-RMM and costly world switch. In this paper, we introduce CubeVisor, a new secure virtualization architecture based on ARM CCA. It uses the idea of the Cube, which is a combination of a hypervisor and a VM, protecting each Cube from other Cubes or components. The CubeVisor also improves performance by optimizing memory allocation and world-switching processes. We implement prototypes on both software-based ARM FVP platform and hardware-based ARM Cortex-A platform for evaluations. The results show that the CubeVisor can protect VMs well and has very low overhead compared to the CCA based virtualization methods.

Summary: This note presents the design of a scalable software package named ImagePy for analysing biological images. Our contribution is concentrated on facilitating extensibility and interoperability of the software through decoupling the data model from the user interface. Especially with assistance from the Python ecosystem, this software framework makes modern computer algorithms easier to be applied in bioimage analysis. Availability and implementation: ImagePy is free and open source software, with documentation and code available at https://github.com/Image-Py/imagepy under the BSD license. It has been tested on the Windows, Mac and Linux operating systems.

The increasingly severe network security situation brings unanticipated challenges to mobile networking. Traditional HMM (Hidden Markov Model) based algorithms for predicting the network security are not accurate, and to address this issue, a weighted HMM based algorithm is proposed to predict the security situation of the mobile network. The multiscale entropy is used to address the low speed of data training in mobile network, whereas the parameters of HMM situation transition matrix are also optimized. Moreover, the autocorrelation coefficient can reasonably use the association between the characteristics of the historical data to predict future security situation. Experimental analysis on DARPA2000 shows that the proposed algorithm is highly competitive, with good performance in prediction speed and accuracy when compared to existing design.