Worm Attack Classification Using Ensemble Classifier and DAG
Journal Title: International Journal for Research in Applied Science and Engineering Technology (IJRASET) - Year 2015, Vol 3, Issue 4
Abstract
the growth of internet technology spread a large amount of data communication. The communication of data compromised network threats and security issues. The network threats and security issues raised a problem of data integrity and loss of data. For the purpose of data integrity and loss of data before 20 year Anderson developed a model of Malware Classification technique. Initially Malware Classification technique work on process of satirical frequency of audit technique logs. Latter on this technique improved by various researchers and apply some other approach such as data mining technique, neural network and expert technique. Now in current research trend of Malware Classification technique used soft computing approach such as fuzzy logic, genetic algorithm and machine learning. For malware classification feature selection is important. The selection of feature in attack attributes and normal traffic attribute is challenging task. The selection of known and unknown attack is also faced a problem of classification. DAG is graph based technique used for the process of feature selection in classification. The acyclic nature of DAG select attribute on selection of entropy. The attribute entropy is high the feature value of DAG network is selected and the attribute value is low the DAG feature selector reduces the value of feature selection. After selection of feature the Gaussian kernel of support vector machine is integrated for classification.
Authors and Affiliations
Abhishek A. Nibe, Avinash B. Anap, Jaydeep T. Arote
Keywords
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