How do spam filters block spam email? Spammers are constantly adapting their strategies to bypass spam filters and deliver more malicious messages to corporate users’ inboxes, so how do antispam solutions keep pace and block these annoying and often malicious messages?
Many anti-spam services rely on blacklists to identify spammers’ email addresses and IP addresses. Once a spammer’s IP address has been identified, it is added to a global spam blacklist.
Antispam solutions check incoming messages against these blacklists. As soon as an IP address is blacklisted, any email sent from that IP address is automatically marked as spam and will be deleted or quarantined.
Spammers are aware that the lifespan of an email address for spamming is short. As anti-spam solutions have improved, the time delay between an email address being used for spamming and it being added to a global spam blacklist has reduced considerably. Whereas spammers used to be able to use an email address for weeks before it was identified by anti-spam solutions and blacklisted, now the lag has been reduced to days or even hours.
Spammers therefore have a very small window of opportunity to use email addresses and mail servers for spamming before they are detected and blacklisted.
Snowshoe and Hailstorm Spam Tactics to Get Messages to Inboxes
Spammers have attempted to increase the timespan for using email addresses using a number of methods, the most common being conducting snowshoe campaigns. This tactic involves sending out very low numbers of spam email messages from each IP address. If spam email volume is kept low, there is less chance of the IP address being recognized as used for spamming. To ensure sufficient numbers of messages are sent, spammers use millions of IP addresses. Even using this tactic will not allow the spammers to conduct their activities undetected for very long. Spammers therefore need to constantly add new IP addresses to their spamming networks to enable them to continue conducting their campaigns.
Snowshoe tactics are now widely used and the technique is highly effective, although a new tactic has recently been uncovered that is referred to as hailstorm spamming. Hailstorm spam campaigns similarly involve extremely large numbers of IP addresses, yet they are used very briefly and intensely. Rather than trying to stay under the radar, the spammers use those IP addresses to send huge volumes of messages very quickly.
Researchers at Cisco Talos recently analyzed both tactics and determined that the DNS query volume from a typical snowshoe campaign involved around 35 queries an hour. A hailstorm spam campaign involved around 75,000 queries an hour. The snowshoe campaign would continue at that rate for many hours, whereas the hailstorm spam campaign spiked and then fell to next to nothing. Hailstorm campaigns can therefore be used to deliver huge volumes of emails before the IP addresses are added to blacklists.
How do Spam Filters Block Spam Email?
How do spam filters block spam email when these tactics are used? Snowshoe and hailstorm spam campaigns are effective against antispam solutions that rely on blacklists to identify spammers. Only when an IP address is added to a blacklist will the spam email messages be blocked. Advanced spam solutions offer far greater protection. Blacklist are still used, although a number of other methods of spam detection are employed.
Conducting a Bayesian analysis on all incoming spam email messages greatly reduces the volume of spam email messages that are delivered to end users. A Bayesian analysis involves reading the contents of a message and assessing the words, phrases, headers, message paths, and CSS or HTML contained in the message. While scoring, messages based on content can be effective, Bayesian spam filters also learn as they go. They constantly compare spam emails to legitimate emails and build up the range of spam characteristics that are checked. As spammers change tactics, this is picked up by a Bayesian spam filter and spam messages continue to be filtered.
The use of greylisting is also important in a spam filter. There will be some messages that pass all of the checks and some that monumentally fail. Categorizing these messages as genuine or spam is therefore simple. However, there is a sizeable grey area – messages that could potentially be spam.
If all of these messages are blocked, many genuine emails would not be delivered. If they are all allowed, many spam messages would get through. This would result in poor catch rates or extremely high false positive rates. Greylisting helps in this regard. Suspect messages are returned to the sender’s mail server and a request is made for the message to be resent. Since spammers mail servers are typically constantly busy, these requests are either ignored or they are not dealt with promptly. The time it takes for the message to be resent is therefore a good indicator of whether the message is genuine.
SpamTitan – Keep Your Inboxes Spam Free
SpamTitan uses a range of methods to identify spam emails including blacklists, Bayesian analyses, and greylisting. These checks ensure that more spam emails are identified and blocked, even if IP addresses have yet to be added to spam blacklists. This makes SpamTitan highly effective, even when spammers use snowshoe and hailstorm spamming tactics. By using a range of methods to identify spam emails, spam detection rates are improved and false positives are reduced.
SpamTitan is independently tested every month to determine its effectiveness. SpamTItan is consistently verified as capable of blocking more than 99.97% of spam emails, with a false positive rate below 0.03%.
If you want to find out the difference that SpamTitan makes to the volume of spam messages that are delivered to your employees’ inboxes, why not take advantage of our free, no-obligation 30-day trial. You can implement the solution quickly, evaluate its effectiveness, and you will receive full customer and technical support for the duration of the trial.