Abstract
Proper classification of the available data into viable Malware classes is very important to analyze the causes, vulnerabilities & intents behind these attacks and to build up systems that are secure from these kinds of attacks. In this paper, we describe an approach that enables us to classify free text documents with good precision and performance. We classify the documents to the malware class that has the highest matching of the characteristics of the document based the ontology model. We have experimented with our integrated approach on a large number of documents and found that it provides a very good classification more precise that than other analysis techniques for documents about malware.
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Notes
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Here, tf represents term-frequency & idf stands for inverse-domain-frequency.
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Rath, M., Agarwal, S., Shyamasundar, R.K. (2017). Semi Supervised NLP Based Classification of Malware Documents. In: Shyamasundar, R., Singh, V., Vaidya, J. (eds) Information Systems Security. ICISS 2017. Lecture Notes in Computer Science(), vol 10717. Springer, Cham. https://doi.org/10.1007/978-3-319-72598-7_21
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DOI: https://doi.org/10.1007/978-3-319-72598-7_21
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