Nowadays, security risk assessment has become an integral part of network security as everyday life has become interconnected with and dependent on computer networks. There are various types of data in the network, often with different criticality in terms of availability or confidentiality or integrity of information. Critical data is riskier when it is exploited. Data criticality has an impact on network security risks. The challenge of diminishing security risks in a specific network is how to conduct network security risk analysis based on data criticality. An interesting aspect of the challenge is how to integrate the security metric and the threat modeling, and how to consider and combine the various elements that affect network security during security risk analysis. To the best of our knowledge, there exist no security risk analysis techniques based on threat modeling that consider the criticality of data. By extending the security risk analysis with data criticality, we consider its impact on the network in security risk assessment. To acquire the corresponding security risk value, a method for integrating data criticality into graphical attack models via using relevant metrics is needed. In this thesis, an approach for calculating the security risk value considering data criticality is proposed. Our solution integrates the impact of data criticality in the network by extending the attack graph with data criticality. There are vulnerabilities in the network that have potential threats to the network. First, the combination of these vulnerabilities and data criticality is identified and precisely described. Thereafter the interaction between the vulnerabilities through the attack graph is taken into account and the final security metric is calculated and analyzed. The new security metric can be used by network security analysts to rank security levels of objects in the network. By doing this, they can find objects that need to be given additional attention in their daily network protection work. The security metric could also be used to help them prioritize vulnerabilities that need to be fixed when the network is under attack. In general, network security analysts can find effective ways to resolve exploits in the network based on the value of the security metric.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:lnu-93055 |
| Date | January 2020 |
| Creators | Zhou, Luyuan |
| Publisher | Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM) |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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