A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA)

作     者：Raja, Edwin S. Ravi, R. 

作者机构：PSR Engn Coll Dept CSE Sivakasi India Francis Xavier Engn Coll Dept Informat Technol Tirunelveli India 

出 版 物：《COMPUTER COMMUNICATIONS》 (计算机通信)

年 卷 期：2020年第153卷第0期

页      面：375-381页

核心收录：

学科分类：0810[工学-信息与通信工程] 0808[工学-电气工程] 08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：This article does not contain any studies with human participants or animals performed by any of the authors 

主　　题：Software Define Network Phishing attack DMLCA approach Text classifier Information retrieval algorithm Performance analysis Hyperlinks Support vector machine 

摘      要：This paper discusses a novel frame work approach of Software Defined Network based prevention on phishing attack with the help of the deep machine learning with CANTINA approach (DMLCA) in the cyberspace. Cyber security is a significant concern in the operations of coalition, and is a complex challenge because of some needs in operational effectiveness and also the trust relationship limit which exists over the coalition partners. In networking, new promising paradigms like Software Defined Networks (SDN), offer a method to deal more efficiently with their security constraints. This machine learning approach is to deal with the phishing attack problem based on the SVM (support vector machine) and this machine learning technique with SVM helps to effectively to solve classification problems. The CANTINA approach helps to support the robust hyperlinks with the help of evaluating the term frequency (TF) and inverse document frequency and (IDF). This information retrieval algorithm helps to compare, classify and retrieve various documents. The objective is to improve the detection accuracy with the help of the DMLCA method with the various parameters such as detection accuracy based on the true positive ratio and false positive ratio, precision and recall.