# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## VMC Implementation and Experiment Design  
The VMC system was implemented on Microsoft Azure(r) Free Tier subscription [64]. Due to encountered limitations, the software was launched in two regions: US West and US West 2 that demonstrated the average latency of 22 ms during tests [65]. In region US West 2 the kubernetes cluster (k8s) was launched [66]. The US West 2 cluster consists of 2 servers each with 2\(\times\) CPU Intel Xeon(r) E5-2673 v3 (Haswell) 2.4 GHz processors and 7 GB RAM memory and supports the following services:  
* VMC processing module
* PostgreSQL database--storing VMC configurations
* MariaDB database--storing CMDB information
* Ralph--CMDB administration panel
* Rabbitmq--queue system used for communication between VMC modules
* Redis--in-memory base used for partial calculations storage and mutex support in VMC modules
* VMC monitor--the monitoring of tasks performed by VMC
* VMC admin panel--module for VMC management In the region of US West Elasticsearch cluster was launched that consists of 2 servers with \(1\times\) CPU Intel Xeon(r) E5-2673 v3 (Haswell) 2.4 GHz processor and 3.5 GB RAM memory. Between the regions US West and US West 2,the network type virtual-network to virtual-network (VLan) has been created. Thus, in summary, all components of the developed VMC run autonomously within a computer cloud environment. This approach allows for performing the vulnerability prioritization in a fully automatic manner. In the following part of this section the numerical experiments are described that show the relevance of each component of the system and the advantage of parallel processing. The application of parallel processing shortens the processing time and allows for an elastic response to increased data processing demand. Thus, in order to show the relevance of automatic integration of the asset collector module, a network model with the distribution of operating systems was used as described in [67]. In order to investigate the behavior and the execution time of the proposed algorithms in the context of smart data, the network model was created containing 2110 IP addresses and had a simplified distribution of operating systems described in Table 1. The network contained 168,940 vulnerabilities of which 3008 vulnerabilities are unique with the distribution presented in Table 2.  
Figure 2 shows CVSS Base histograms for the proposed model. With this set of vulnerabilities, three configurations of the CIA distributions as described in Tables 3-5 were studied. \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Value** & **Name** & **Value** \\ \hline Redhat 5 & 18.48\% & IBM AIX 6 & 4.27\% \\ Redhat 6 & 19.67\% & IBM AIX 5 & 5.69\% \\ Redhat 7 & 18.96\% & IBM AIX 7 & 4.03\% \\ Windows Server 2016 & 7.82\% & Debian 8 & 2.13\% \\ Windows Server 2019 & 8.06\% & Debian 9 & 1.41\% \\ Windows Server 2012 & 7.58\% & Debian 10 & 1.9\% \\ \hline \end{tabular}
\end{table} 
Table 1: Distribution of operating systems. \begin{table}
\begin{tabular}{c c c c} \hline
**Name** & **Value** & **Name** & **Value** \\ \hline Redhat 5 & 6.53\% & IBM AIX 6 & 0.8\% \\ Redhat 6 & 27.33\% & IBM AIX 5 & 1.11\% \\ Redhat 7 & 26.46\% & IBM AIX 7 & 1.01\% \\ Windows Server 2016 & 13.11\% & Debian 8 & 2.32\% \\ Windows Server 2019 & 8.6\% & Debian 9 & 1.8\% \\ Windows Server 2012 & 10.03\% & Debian 10 & 0.9\% \\ \hline \end{tabular}
\end{table} 
Table 2: Distribution of vulnerabilities with operating systems division. Figure 2: CVSS Base histograms. Then, to test the advantages of vertical scaling for the processing module, the time gap between an occurrence of a case (\(P\)) and obtaining the final results concerning the CVSS environmental assessment was measured.