Abstract: Pattern classification is one division for machine learning that focuses on recognition of patterns and regularities in data.In adversarial applications like biometric authentication, spam filtering, network intrusion detection the pattern classification systems are used. Pattern classification systems may exhibit vulnerabilities if adversarial scenario is not taken into account. Multimodal biometric systems are more robust to spoofing attacks,as they combine information coming from different biometric traits. In this paper,we evaluate the security of pattern classifiers that formalizes and generalizes the main ideas proposed in the literature and give examples of its use in three real applications. We propose a framework for evaluation of pattern security, model of adversary for defining any attack scenario. Reported results show that security evaluation can provide a more complete understanding of the classifier’s behaviour in adversarial environments, and lead to better design choices.
Keywords: Data mining, Pattern classification, Model of adversary.
Title: ATTACKS ERADICATION TECHNIQUE USING SPAM FILTER WITH PATTREN CLASSIFICATION
Author: GOPINADH.ADAPA, S.UMAMAHESWARA RAO
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), SSN 2348-120X (online)
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