Nowadays, almost all email service providers can automatically detect spams emails in user accounts effectively and redirect those potential spam emails to spam folders without human intervention. But, how are spam emails detected automatically by email service providers? Almost all email service providers use machine learning to detect these spam emails. Typically, this machine learning technique relies on some predefined rules. When an incoming email matches most of those rules, the email is marked as spam and redirected to spam folders automatically.
Almost one billion people's personal data has been breached online by a shadowy marketing company that has since disappeared without a trace. Email addresses from 982 million people were listed in what researchers are calling one of the'biggest and most comprehensive email database' breaches ever. Personal information including names, gender, date of birth, employer, details of social media accounts and even home addresses were listed. Security researchers uncovered the breach in an online database created by Verifications.io that had no privacy protections in place. The firm offered an'enterprise email validation' service that let other marketing firms check whether lists of email addresses they have harvested are real.
Email remains the workhorse of digital marketing. But how do you create email campaigns that increase conversions, drive more traffic to your website and generate more sales? Here are insights from top marketers on automation, segmentation and personalization -- six big email marketing tips from the best in the banking industry. Personalization, a hot topic in all areas of digital marketing, is absolutely essential in email. Even though the medium is ideally suited to personalization, many financial marketers still regard email as a "one-to-many" tool to broadcast messages.
To date, most studies on spam have focused only on the spamming phase of the spam cycle and have ignored the harvesting phase, which consists of the mass acquisition of email addresses. It has been observed that spammers conceal their identity to a lesser degree in the harvesting phase, so it may be possible to gain new insights into spammers' behavior by studying the behavior of harvesters, which are individuals or bots that collect email addresses. In this paper, we reveal social networks of spammers by identifying communities of harvesters with high behavioral similarity using spectral clustering. The data analyzed was collected through Project Honey Pot, a distributed system for monitoring harvesting and spamming. Our main findings are (1) that most spammers either send only phishing emails or no phishing emails at all, (2) that most communities of spammers also send only phishing emails or no phishing emails at all, and (3) that several groups of spammers within communities exhibit coherent temporal behavior and have similar IP addresses. Our findings reveal some previously unknown behavior of spammers and suggest that there is indeed social structure between spammers to be discovered.
In this document, we explore how semantic technologies can be brought to email addressing. We introduce the notion of semantic email addressing (SEA). SEA allows emails to be sent to a semantically specified recipient of group of recipients, which may be dynamically changing over time. We give some applications of SEA and describe our prototype implementation.