Cloud computing environments consist of many entities that have different roles, such as provider and customer, and multiple interactions amongst them. Trust is an essential element to develop confidence-based relationships amongst the various components in such a diverse environment. The current chapter presents the taxonomy of trust models and classification of information sources for trust assessment. Furthermore, it presents the taxonomy of risk factors in cloud computing environment. It analyses further the existing approaches and portrays the potential of enhancing trust development by merging trust assessment and risk assessment methodologies. The aim of the proposed solution is to combine information sources collected from various trust and risk assessment systems deployed in cloud services, with data related to attack patterns. Specifically, the approach suggests a new qualitative solution that could analyse each symptom, indicator, and vulnerability in order to detect the impact and likelihood of attacks directed at cloud computing environments. Therefore, possible implementation of the proposed framework might help to minimise false positive alarms, as well as to improve performance and security, in the cloud computing environment.
|Title of host publication||Guide to Vulnerability Analysis for Computer Networks and Systems|
|Subtitle of host publication||An Artificial Intelligence Approach|
|Editors||Simon Parkinson, Andrew Crampton, Richard Hill|
|Publisher||Springer International Publishing AG|
|Publication status||Published - 8 Sep 2018|
|Name||Computer Communications and Networks|
Chrysikos, A., & McGuire, S. (2018). A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attack Pattern Detection. In S. Parkinson, A. Crampton, & R. Hill (Eds.), Guide to Vulnerability Analysis for Computer Networks and Systems: An Artificial Intelligence Approach (pp. 81-99). (Computer Communications and Networks). Springer International Publishing AG. https://doi.org/10.1007/978-3-319-92624-7_4