Unwanted SMTP Paths and Relays

Abstract

Based on the social interactions of an email user, category. Phishing attackers masquerade the identity of a incoming email traffic can be divided into different categories genuine sender and steal consumers' personal identity data such as, telemarketing, Opt-in family members and friends. Due and financial account credentials. Phishers send spoofed emails to a lack of knowledge in the different categories, most of the existing spam filters are prone to high false positives and false and lead consumers to counterfeit websites designed to trick negatives. Moreover, a majority of the spammers obfuscate their the recipients into divulging their financial information such email content inorder to circumvent the content-based spam as, credit card numbers, account user names, passwords, and filters. However, they do not have access to all the fields in the social security numbers. Legislation cannot help because these email header. Our classification method is based on the path attacks can originate outside the United States.traversed by email (instead of content analysis) since we believe that spammers cannot forge all the fields in the email header. WeWe need a solution for unsolicited emails. The solution based our classification on three kinds of analyses on the header: should consider other aspects of the email such as, information i) EndToEnd path analysis, which tries to establish the legitimacy about how an email is routed from a source to the destination of the path taken by an email and classifies them as either spam besides considering the content of an email. In essence, the or non-spam; ii) Relay analysis, which verifies the trustworthiness reputation and trustworthiness of the relays and the path of the relays participating in the relaying of emails; iii) Emails traversed by an email have to be taken into consideration. wantedness analysis, which measure the recipients wantedness of the senders emails. We use the IMAP message status flags The information about the paths and relays can be obtained such as, message has been read, deleted, answered, flagged, and from the email header. Spammers can obfuscate the content draft as an implicit feed back from the user in Emails wantedness of an email but they cannot manipulate all the fields of the analysis. Finally we classify the incoming emails as i) socially close email header. Our classification method is based on the path (such as, legitimate emails from family, and friends), ii) socially distinct emails from strangers, iii) spam emails (for example, traversed by an email. Though spammers can forge certain emails from telemarketers, and spammers) and iv) opt-in emails. fields of the email header (for example, spammers can insert Based on the relation between spamminess of the path taken by spurious Received: header lines before dispatching the spam spam emails and the unwantedness values of the spammers, we email), they cannot modify the complete path traversed by classify spammers as i) prospective spammers, ii) suspects, iii) an email. Hence we rely on th...