Classification of Hospital Web Security Efficiency Using Data Envelopment Analysis and Support Vector Machine

Abstract

This study proposes the hybrid data envelopment analysis (DEA) and support vector machine (SVM) approaches for efficiency estimation and classification in web security. In the proposed framework, the factors and efficiency scores from DEA models are integrated with SVM for learning patterns of web security performance and provide further decision support. The numerical case study of hospital web security efficiency is demonstrated to support the feasibility of this design.

A Network DEA Model with Super Efficiency and Undesirable Outputs: An Application to Bank Efficiency in China

“…1− * ( 1+ * ) is the optimal slacks in the inputs (outputs) of stage 1 (2). * ( * ) is the optimal slacks in good (bad) outputs of stage 2 for (6).…”

“…Since it was first developed by Charnes et al [1], DEA (data envelopment analysis) has been widely used to measure the performance of DMUs (decision making unites) that convert multi-inputs into multi-outputs, such as bank performance [2,3], company performance [4,5], hospital web security [6], production planning [7], energy consumption productivity [8], bankruptcy assessment [9], electricity distribution [10], R&D performance [11], agricultural economics [12], airport performance [13], and other applications [14]. In traditional DEA models, DMU is treated as a "black box, " in which the inputs enter and outputs exit, neglecting the intervening steps [15].…”

An efficient solution for switching web security training scenarios on Windows platform

“…Web security is to test the vulnerability of a website, find out the vulnerability of the website, and prevent the website from being hacked [3] . Deploying a real website with vulnerabilities in a virtual machine, this environment is called a "web security range".Web security range is a very important training environment for students to dig into web vulnerabilities and master web security techniques [4] .…”

Missing Values Treatment and Feature Reduction Analysis to Enhance Classification

“…But here we concern with only the supervised learning which uses the class attribute and therefore called classification models. Support Vector Machine has attracted much attention recently and has been successfully used in various applications Cortes and Vapnik (1995;Kao et al, 2013;Muthu Rama Krishnan et al, 2010;Xie and Wang, 2011;Burbidge et al, 2001). As SVM produces accurate classification for both linear and non-linear relationships, it is preferred over other classifiers.…”