How AI weeds the spam out of our inboxes

of more More than 300 billion emails Sent every day, At least half Are spam. Email providers have a huge job of filtering spam and getting messages to their users to make sure they matter.

Spam detection is messed up. The line between spam and non-spam messages is fake, and the parameters change over time. With various efforts to automate spam detection, machine learning By far the most effective and preferred approach has been proved by email providers. Although we still see spam email, a quick glance at the junk folder will show how much spam weed comes out of our inbox every day thanks to machine learning algorithms.

How does machine learning determine which emails are spam and which are not? Here is an overview of how machine learning-based spam detection works.

the challenge

Spam email comes in various flavors. Many are just annoying messages aimed at attracting attention to a cause or spreading false information. some of them are Phishing email Intended to entice the recipient into clicking malicious links or downloading malware.

They have one thing in common that they are irrelevant to the needs of the recipient. Spam-detector algorithms have to find a way to filter spam and at the same time avoid flagging authentic messages that users want to see in their inboxes. And it must do so in a way that can match emerging trends such as pandemics, electoral news, sudden interest in cryptocurrency, and panic caused by others.

Static rules can help. For example, too many BCC recipients, too little body text, and all caps are some signs of a subject spam email. Similarly, some sender domains and email addresses may be associated with spam. But for the most part, spam detection mainly depends on analyzing the content of the message.

Now Base Machine Learning

Machine learning algorithms use statistical models to classify data. In the case of spam detection, a trained machine learning model should be able to determine if the order of the words found in the email is close to the spam email or the secure ones.

Different machine learning algorithms can detect spam, but the one that has gained appeal is the “naive Bayes” algorithm. As the name itself suggests, the naive base is “based on”Bayes’ theorem, “Which describes the probability of an event based on prior knowledge.

Bayes theorem