Spam Filtering Security Evaluation Framework Using SVM, LR and MILR

The Pattern classification system classifies the pattern into feature space within a boundary. In caseadversarial applications use, for example Spam Filtering, the Network Intrusion Detection System (NIDS),Biometric Authentication, the pattern classification systems are used. Spam filtering is an adversaryapplication in which data can be employed by humans to attenuate perspective operations. To appraise thesecurity issue related Spam Filtering voluminous machine learning systems. We presented a framework forthe experimental evaluation of the classifier security in an adversarial environments, that combines andconstructs on the arms race and security by design, Adversary modelling and Data distribution underattack. Furthermore, we presented a SVM, LR and MILR classifier for classification to categorize email aslegitimate (ham) or spam emails on the basis of thee text samples.