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A Framework for Spam Filtering Security Evaluation

Kunjali Pawar, Madhuri Patil


The Pattern classification system is an annex of the Machine learning on which the focal point is the recognition of patterns in the data. In case adversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS), Biometric Authentication, the pattern classification systems are used. Spam filtering is and adversary application in which data can be employed by humans to attenuate perspective operations. To appraise the security issue related Spam Filtering voluminous machine learning systems. We presented a framework for the experimental evaluation of the classifier security in an adversarial environments, that combines and constructs on the arms race and security by design, Adversary modelling and Data distribution under attack. Furthermore, we presented a MILR classifier with SVM and LR classifier for classification to categorize among legitimate and spam emails on the basis of their textual content.


Adversary Model, Pattern Classification, Security Evaluation, Spam Filtering.

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