Department of Information Technology

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Subject: search.filters.subject.SVM

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    SPIDERNET: AN INTERACTION TOOL FOR PREDICTING MALICIOUS WEB PAGES
    (P S G R Krishnammal College for Women, 2015-02) Krishnaveni S; Sathiyakumari K
    Malicious code injection poses a serious security issue over the Internet or over the Web application. In malicious code injection attacks, hackers can take advantage of defectively coded Web application software to initiate malicious code into the organization's systems and network. The vulnerability persevere when a Web application do not properly sanitize the data entered by the user on a Web page. Attacker can steal confidential data of the user like password, pin number, and etc., these attacks resulting defeat of market value of the organization. This research work is model the malicious Web page prediction as a classification task and provides a convenient solution by using a powerful machine learning technique such as Support Vector Machine (SVM), Extreme Learning Machine (ELM). The main aim of this research work is to predict the type of the malicious attack like Redirect, Script injection and XSS using the machine learning approaches; in this case, the prediction time is taken into consideration. The supervised learning algorithms such as SVM and ELM are employed for implementing the prediction model.
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    FUZZY LOGIC BASED IMPROVED SUPPORT VECTOR MACHINE (FISVM) CLASSIFIERFOR HEART DISEASE CLASSIFICATION
    (ARPN Journal of Engineering and Applied Sciences, 2015-09) S, Nithya; C, Suresh Gnana Dhas
    Classification is the major research topic in data mining. Typically classification represents the data to be categorized based on its features or characteristics. This proposed research work aims in developing fuzzy logic based improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine by making use of fuzzy logic. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-AlizadehSani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed fuzzy logic based improved support vector machine classifier (F-ISVM) with several classification algorithms. Results showed that the proposed F-ISVM classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.
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    AN IMPROVED SUPPORT VECTOR MACHINE (I-SVM) CLASSIFIER FOR HEART DISEASE CLASSIFICATION
    (International Journal of Applied Engineering Research, 2015) S, Nithya; C, Suresh Gnana Dhas
    Classification is the major research issue in data mining. Usually classification represents the data to be categorized based on its features or characteristics. This research work aims in developing an improved support vector machine classifier. Support vector machine is a type of supervised machine learning technique and once when the dataset is given as input it performs the classification task by itself. The proposed classifier aims in improving the classification accuracy of the support vector machine. The proposed classifier has been tested on two different datasets namely PIMA Indian diabetes dataset and Z-Alizadeh Sani dataset in order to classify the occurrence of heart disease among the patients. Performance metrics sensitivity, specificity and classification accuracy are taken for comparison of the proposed improved support vector machine classifier (I-SVM) with several classification algorithms. Results showed that the proposed classifier gives better classification accuracy than that of support vector machine, naive bayes, neural networks, sequential minimal optimization (SMO) and bagging SMO classifiers.