A Predictive Model for Risk and Trust Assessment in Cloud Computing: Taxonomy and Analysis for Attack Pattern Detection

Alexandros Chrysikos, Stephen McGuire

Research output: Chapter in Book/Report/Conference proceedingChapterpeer-review


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.
Original languageEnglish
Title of host publicationGuide to Vulnerability Analysis for Computer Networks and Systems
Subtitle of host publicationAn Artificial Intelligence Approach
EditorsSimon Parkinson, Andrew Crampton, Richard Hill
PublisherSpringer International Publishing AG
ISBN (Electronic)9783319926247
ISBN (Print)9783319926230
Publication statusPublished - 8 Sep 2018

Publication series

NameComputer Communications and Networks
ISSN (Print)1617-7975
ISSN (Electronic)2197-8433


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