AI and spam filtering: How AI will help fight Email Spam

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Syed Balkhi
Syed Balkhi
Syed Balkhi is the founder of WPBeginner, the largest free WordPress resource site. With over 10 years of experience, he’s the leading WordPress expert in the industry. You can learn more about Syed and his portfolio of companies by following him on his social media networks.

Many people experience email spam as an annoying but non-threatening experience. This testifies to the fact that today, there are powerful tools and platforms working hard to protect users from email spam.

According to Statista’s spam statistics for 2019:

  • 281.1 billion emails were sent and received daily in 2018, including billions of marketing emails sent by marketers. 
  • Only 85 percent of marketing emails landed in a customer’s inbox. A mere 7% were caught by spam filters.

While issues related to marketing emails may be annoying but not harmful, there’s a very real danger of malicious spam emails. As a reminder, here are more spam stats that show how spam negatively impacts individuals and businesses:

  • Spam uses up email storage capacity and affects email server memory space, power, and more
  • Unwanted spam forms 77% of the total global email traffic
  • People have been financially and emotionally victimized by phishing emails that stole their personal information 
  • Malicious spam emails can contain malware that breaks into a user’s compute

Spam technology needs to be robust enough to target and filter harmful spam but without creating unnecessary losses for businesses. 

The filtering of spam, done without consideration, can lose businesses billions of dollars. Nonprofit organizations lose $15k/year in donations due to spam filters blocking fundraising campaign emails. The significance of these facts is that there’s an urgent need for AI-based filtering tools to clean up spam.

Typically, individuals and organizations protect their inboxes from spam by using Recaptcha technology, by adding a ‘Honeypot’ field to forms, or by whitelisting one’s email with an SMTP mail tool. For websites using WordPress, the plugin Akismet offers spam protection in comments. It’s one of the most commonly used tools by non-experts with over 5 million WordPress installations.

Spam technology as a double-edged sword

While the tools mentioned below provide a measure of security against spam, there’s a need for more intelligent spam management. Here are two ways that managing spam can be a double-edged sword.

One, a spam filtering tool must unfailingly prevent spam from entering a person’s inbox and at the same time avoid mislabeling harmless business communication.

Two, AI is a powerful tool to protect people from spam. But the same AI can be used by attackers to make their tactics more efficient. Attackers and hackers can also use AI to make communication more personalized and to scale the accuracy and frequency of their attacks. 

The development of AI spam technology needs to consider these opposing concerns. Fortunately, the very nature of machine learning technology offers solutions that can help solve the problems we’ve just covered. 

AI to power spam-prevention technology

Today, Google uses TensorFlow to block 100 million spam emails a day. The use of machine learning means that there’s a transition from pattern recognition in spam emails to self-learning and optimizing systems. 

Here are ways that AI-based tools will detect and filter spam: 

Keyword and content-based filtering: Machine learning approaches such as Neural Networks, Naïve Bayesian classification, k-nearest neighbor(kNN), and others are used. Here, keywords, phrases, and their distribution and frequency are assessed and rules are made to filter spam email. 

Similarity-based filtering: Here, kNN is used to classify emails based on whether they are similar to stored emails. Email attributes form a foundation and based on these, new instances are plotted as points for future emails. 

Sample-based filtering: Machine learning algorithms are trained to detect new emails as spam or not based on training data extracted from sample mails. These sample emails are from legitimate and spam emails.  

Adaptive email spam filtering: Here, spam emails are made into groups. Each group is represented by a token or emblematic text. These groups of representative texts are made up of words, phrases, and even meaningless strings. Incoming email is compared to these tokens or representative text and classified into spam or not-spam.

These are some of the ways that machine learning approaches are used to resolve the problem of spam. The promising aspect of using AI is that we can expect the algorithms to adapt and improve over time, ensuring that individual and legitimate business concerns over email security are met. 


The truth is that IT security matters more than ever. Hackers are getting smarter and more efficient just as AI technology and tools are becoming more commonplace. 

Virus signatures and attack patterns are changing rapidly, faster than can be managed with the technology we’ve used so far. 

We need a self-learning solution to manage these threats and AI holds the key. By continuing to invest in machine learning and related technologies, businesses and individuals can be assured of their business growth and safety. 

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