# Efficient Algorithm for Providing Live Vulnerability Assessment in Corporate Network Environment
## 3 Related Work  
Many authors [42; 43] stress the fact that in order to prioritize the vulnerability correctly, organizations should consider an asset value and a vulnerability importance in a standardized way. The problem of vulnerability prioritization has been discussed in available literature for a long time [44; 45; 46; 47]. Most established companies that take cybersecurity seriously into consideration have a vulnerability management process implemented to a greater or lesser extent [3; 42; 44]. However, as noticed in [3; 44; 45; 46; 47] each organization approaches the problem differently. The solutions enlisted in the report [48] F-Secure [6], Qualys [7], Rapid7 [8], or Tenable [9], on the one hand help the organizations to overcome the vulnerability management problem but on the other one, they have two drawbacks. Namely, they are very expensive and they do not inform users on the details of the prioritization procedure. For instance, Qualys uses a 7-point scale [7], Rapid7 performs the prioritization in the range from 1 to 1000 [8], whereas Tenable named its prioritization method VPR and the provided levels range from 1 to 10 [9]. Next to commercial solutions, it is possible to find in literature, other solutions, i.e.,: PatchRank [49], SecureRank [50], VULCON [37], or VEST [51]. The PatchRank solution focuses only on the updates prioritization for SCADA systems [49]. The SecureRank uses the network topology and the potential interaction between servers to calculate their risk [50]. The Vulcon's software strategy is based on two elementary pointers: the vulnerability appearance time (TVR) and total vulnerability exposure time [37]. VEST, however, focuses on verifying whether the vulnerability is exploited and how quickly it can be used by an attacker [51]. Other solutions, e.g., [37; 49; 50; 51]do not take into consideration the value of assets and are not adjusted to the increasing amount of data in cloud computing environment and therefore they cannot be applied in every network infrastructure. Additionally, none of the presented solutions offer prioritization for CVSS 2.0 and CVSS 3.1 simultaneously. This is due to the fact that not all vulnerabilities were converted from CVSS 2.0 to CVSS 3.1, even though CVSS 3.1 assesses the essence of vulnerability in a better way and estimates threats more efficiently [18]. An important characteristic of the VMC developed in this contribution is solving the problem of scalability. Thus, allowing to adjust the tool easily to the increasing amount of incoming data. In comparison to [37; 49; 50; 51] the developed VMC uses the information collected from the asset database. Unlike [6; 7; 8; 9] the prioritization procedure has been outlined in detail and hence can be analyzed using FIRST standard [14]. Another aspect of the developed VMC's operational activities is handling the big sets of data. According to results and discussions presented in available literature, a significant element of managing big sets of data is their life cycle. In [52] the authors examined over a dozen different data life cycles and their phases aiming to find the ones that \"makes data Smart and thus facilitate their management in the Big Data context\". \"Smart\" meaning the aforementioned knowledge obtained from the big and initially unstructured data set. According to [11] \"the phases which constitute the data life cycle in a Big Data context are very complex. Each phase is considered as one or more complex, operational, and independent processes, but these processes are linked to one another and to make data management more flexible and smart\". Thus, presenting the data life cycle is not a trivial task. The developed VMC as an open source product is characterized by transparency of the used methods and techniques. The developed VMC has been based on, so-called, Smart Data Lifecycle presented in [11]. Its aim is to present the data life cycle as a process, in order to increase its flexibility and ability of adjusting to different cases.