Today the use of electronic messaging systems is wide spread. Email, text messaging, instant messaging, live chatting, etc. have become very common in everyday life to the point that nearly everyone uses some form of electronic messaging on a daily basis. Assuring the delivery, authenticity, and integrity of messages is very important to all senders and recipients, and particularly for senders who send messages to recipients for marketing or transactional purposes.
Messaging systems are often abused and used to distribute unwanted or undesirable messages (or other network traffic), which are commonly referred to as spam. Spam can refer to the practice of sending unwanted messages, frequently with commercial content, in large quantities to an indiscriminate set of recipients. One non-limiting example of spam is unsolicited bulk email, otherwise known as spam email or junk email. Spamming remains economically viable because advertisers have no operating costs beyond the management of their mailing lists, servers, infrastructures, IP ranges, and domain names, and it is difficult to hold senders accountable for their mass distribution of messages. Because the barrier to entry is so low, spammers are numerous, and the volume of unsolicited messages has become very high.
To combat spam, many different anti-spam techniques have been developed to distinguish between solicited or wanted messages, and unsolicited or unwanted spam messages. Anti-spam techniques can include end-user techniques that require actions by individuals, automated techniques for email administrators, and automated techniques for email senders. Some examples of automated techniques for email administrators include algorithmic filters and message authentication.
One unintended drawback of many existing solutions for distinguishing between “wanted” messages and spam messages is that they tend to produce false positives (e.g., “good” messages are marked as spam) and false negatives (e.g., “bad” messages are not marked as spam). In other words, many existing solutions can incorrectly identify a “wanted” message that a user wants to receive as being spam email and classify it as such (e.g., place it in a spam folder). Conversely, many existing solutions can incorrectly identify a spam message that a user does not want to receive and allow it to be forwarded to the user's inbox.
Each existing anti-spam technique has trade-offs between incorrectly rejecting legitimate messages (false positives) versus not rejecting all spam (false negatives). As such, there is a need for improved electronic messaging systems and technologies for delivering electronic messages.
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