Social network based filtering of unsolicited messages from e-mails

Nowadays Electronic communication is an important medium and an inevitable way for official communication. So, the email classification into spam or ham gains a lot of importance. Commonly used approaches are text-based or collaborative methods for spam detection. However, not only choosing the right classifier is very difficult but, handling poison attacks and impersonation attacks are also very important. The proposed model considers a powerful spam filtering technique which includes both social network and email factors in addition to the email data analysis for spam classification. The incoming emails are subjected to header parsing for finding the trust and reputation of senders with respect to the receivers and keyword parsing is applied to find the topic of interest using LDA with Gibbs Sampling method. Optical Character Recognition (OCR) method is applied to find the image spam e-mails. Degree and strength of the connection between the users from the social networks are also considered along with the email data factors for better message classification. Logistic Regression is used to combine all the independent input features to get an effective result. The experimental results and comparisons with the existing models vividly show the significant performance of the proposed classifier.