Virtualization of operating systems and network infrastructure plays an important role in current IT projects. With the number of services running on different hardware resources it is easy to provide availability, security and efficiency using virtualizers. All virtualization vendors claim that their hypervisor (virtual machine monitor - VMM) is better than their competitors. In this paper we evaluate performance of different solutions: proprietary software products (Hyper-V, ESXi, OVM, VirtualBox), and open source (Xen). We are using standard benchmark tools to compare efficiency of main hardware components, i.e. CPU (nbench), NIC (netperf), storage (Filebench), memory (ramspeed). Results of each tests are presented.

The enormous growth of services and data transmitted over the internet, the bloodstream of modern civilization, has caused a remarkable increase in cyber attack threats. This fact has forced the development of methods of preventing attacks. Among them, an important and constantly growing role is that of machine learning (ML) approaches. Convolutional neural networks (CNN) belong to the hottest ML techniques that have gained popularity, thanks to the rapid growth of computing power available. Thus, it is no wonder that these techniques have started to also be applied in the network traffic classification domain. This has resulted in a constant increase in the number of scientific papers describing various approaches to CNN-based traffic analysis. This paper is a survey of them, prepared with particular emphasis on a crucial but often disregarded aspect of this topic—the data transformation schemes. Their importance is a consequence of the fact that network traffic data and machine learning data have totally different structures. The former is a time series of values—consecutive bytes of the datastream. The latter, in turn, are one-, two- or even three-dimensional data samples of fixed lengths/sizes. In this paper, we introduce a taxonomy of data transformation schemes. Next, we use this categorization to describe various CNN-based analytical approaches found in the literature.

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