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- W2989591782 abstract "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." @default.
- W2989591782 created "2019-12-05" @default.
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- W2989591782 date "2020-03-01" @default.
- W2989591782 modified "2023-10-01" @default.
- W2989591782 title "A performance analysis of Software Defined Network based prevention on phishing attack in cyberspace using a deep machine learning with CANTINA approach (DMLCA)" @default.
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- W2989591782 doi "https://doi.org/10.1016/j.comcom.2019.11.047" @default.
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