An effective computational technique for taxonomic position of security vulnerability in software development

An increasing demand of security standards in open networks and distributed computing environment has become a critical issue for automation of the business process workflow. At automation level, it is a challenging task to methodically analyze the security constraint during the composition of business process component. For the complete automation of business process, one must scrutinize the flow of security patterns, which consist of the bit value of the respective parameter, which is the key entity for identifying the security vulnerability. Various phase-wise security patterns have been used to identify the security vulnerabilities during the black/white box testing phase of the service development. In respect of automation in business logic, this article introduces a machine learning computational technique that classifies the possible types of phase-wise class categories of security vulnerability. The performance matrix along with comparative analysis suggests that the proposed approach proficiently matches the attack pattern to respective security pattern, which can classify phase-wise class categories of security vulnerability in software component development. (C) 2017 Elsevier B.V. All rights reserved.