The Novel Pattern Classification over Performance Security Robustness Evaluation

Abstract

Pattern classification is a branch of 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. Our research paper consists of comprehensive study of spam detection algorithms under the category of content predicated filtering and rule predicated filtering. The implemented results have been benchmarked to analyze how accurately they have been relegated into their pristine categories of spam and ham. Further, an incipient filter has been suggested in the proposed work by the interfacing of rule predicated filtering followed by content predicated filtering for more efficient results. The system evaluates at design phase the security of pattern classifiers, namely, the performance degradation under potential attacks they may incur during operation. A framework is used for evaluation of classifier security that formalizes and generalizes the training and testing datasets. As this antagonistic situation is not considered by traditional configuration techniques, design transfer frameworks may show susceptibilities, whose abuse might astringently influence their execution, and subsequently restrain their commonsense utility. Extending example assignment hypothesis and configuration routines to antagonistic settings is subsequently a novel and exceptionally germane examination bearing, which has not yet been pursued in an efficient way.