Analysis of Spam Detection Using Integration of Logistic Regression and PSO Algorithm

Content-based text classification system can automatically categories the text document into predefined limited classes. But the e-mail document classification is a challenging process in the modern internet environment. The e-mail documents are lightly signified in a great dimensional features space, creating a learning process, and the generalization (abstraction) process is problematic. Spam is unsolicited mail, the bulk of spam mails sent by the spammers who use vast e-mail programs to cover their characteristics and send the spam mails every day with no money. The spam mail directs various effects, including exposing unwanted images, decreasing company productivity, blocking Internet Service Providers' (ISP) networks, etc. Additionally, the spam mail contains an infection, which is gotten ready for some fake movement. Newline PSO is mighty in taking care of multivariable issues, where factors take on genuine qualities that are taken as new lines an independent method to arrange the datasets and contemplate the PSO-based Classifier for Multiclass Data Sets. Spam has become the foundation of decisions utilized by digital crooks to spread vindictive payloads, for example, infections and Trojans. Community-oriented spam recognition methods can manage the enormous scope of electronic communication information subsidized through numerous causes, and special issues needing exposure to electronic communication information. Distance-protecting hash standard arrangements utilized to save the security of electronic communication information while empowering mails orders to detect spam recognition. PSO, a Big Data security safeguarding synergistic spam identification stage based on top of a standard Map Reduce office. Without PSO feature selection, the training accuracy would be less. With the help of the best dataset fit, the classification result will be high.