Title
Automatic Verification and Diagnosis of Security Risk Assessments in Business Process Models.
Abstract
Organizations execute daily activities to meet their objectives. The performance of these activities can be fundamental for achieving a business objective, but they also imply the assumption of certain security risks that might go against a company's security policies. A risk may be defined as the effects of uncertainty on the achievement of the goals of a company, some of which can be associated with security aspects (e.g., data corruption or data leakage). The execution of the activities can be choreographed using business processes models, in which the risk of the entire business process model derives from a combination of the single activity risks (executed in an isolated manner). In this paper, a risk assessment method is proposed to enable the analysis and evaluation of a set of activities combined in a business process model to ascertain whether the model conforms to the security-risk objectives. To achieve this objective, we use a business process extension with security-risk information to: 1) define an algorithm to verify the level of risk of process models; 2) design an algorithm to diagnose the risk of the activities that fail to conform to the level of risk established in security-risk objectives; and 3) the implementation of a tool that supports the described proposal. In addition, a real case study is presented, and a set of scalability benchmarks of performance analysis is carried out in order to check the usefulness and suitability of automation of the algorithms.
Year
DOI
Venue
2019
10.1109/ACCESS.2019.2901408
IEEE ACCESS
Keywords
Field
DocType
Business process management business process model security-risk assessment model-based diagnosis constraint programming
Business process,Computer science,Process modeling,Risk assessment,Risk analysis (engineering),Automation,Data Corruption,Business process modeling,Security policy,Scalability,Distributed computing
Journal
Volume
ISSN
Citations 
7
2169-3536
0
PageRank 
References 
Authors
0.34
0
4