What is Spam Filtering and How Does It Work?

Spam filtering is a feature in email and email security programs designed to help protect an organization against unwanted, bulk emails. It identifies likely spam entering an organization’s network and drops it before it reaches the intended recipient’s inbox.

Most spam traffic is unwanted, but some spam can be malicious, containing malware or phishing content. While blocking all spam is desirable, it’s especially important that this malicious traffic be identified and blocked before it tricks the recipient or infects their computer.

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How Does It Work?

Spam filters are built into email and security programs that are deployed in line with an organization’s email program. When email traffic enters or leaves the organization’s network, it is inspected for various types of potential threats.

Spam filtering looks for potential red flags in inbound email traffic. For example, it might identify and block traffic coming from domains known to be associated with spammers. If the email scanner determines that an email is likely spam, it will label it as such. This will prevent the spam email from being delivered to the recipient’s inbox, instead landing in their spam folder. This way, the user is forewarned about the potential threat and, if they check their spam folder, can make an informed determination about whether or not the email is legitimate.

The Different Types of Spam Filters

Spam filters can work in a variety of different ways, and some of the most widely used types include:

Content Filters: Content-based spam filters inspect the content of emails for potential signs that they are spam. For example, spam emails often use certain words, phrases, and formatting. By looking for these red flags, content-based filters can identify likely spam with a high degree of confidence.
Bayesian Filters: Bayesian filters perform content analysis but integrate a machine learning component to increase their effectiveness over time. As the email scanner inspects spam and legitimate emails, it models the probability that certain words or phrases will appear in each. This enables it to score emails based on their likelihood of containing spam with increasing accuracy over time.
Header Filters: Emails contain headers that include metadata, such as the source, destination, and route taken by an email. Analysis of these headers can help with the identification of spam traffic. For example, spammers might try to obfuscate the source of the traffic or the route that it took to the corporate email server. If these modifications are detected, they’re a warning sign of spam or other malicious emails.
Blocklist Filters: Certain domains and IP addresses are known spammers whose information is shared via threat intelligence feeds. If an email scanner identifies an email coming from one of these domains or IPs, it can confidently label it as spam. However, this type of filter can only protect against known spammers, not zero-day threats.
Custom Rule Filters: Organizations can also implement custom rules for their spam filters. For example, these could include senders or keywords that should be blocked from reaching an organization’s inboxes.

Spam Filtering with AI

The rise of generative AI offers new potential for spam filtering. GenAI tools like ChatGPT have the ability to understand text and provide a response based on it. More generally, AI can analyze large volumes of data and extract patterns or anomalies from it.

This capability offers the potential to dramatically enhance an organization’s spam filtering capabilities. Some of the applications of AI for spam filtering include:

Content Analysis: Traditional content filters work based on keywords and phrases. By incorporating AI and natural language processing (NLP), spam filters can understand emails like a human would, enabling them to determine the desired impact of the email or if it “sounds like” spam.
Header Analysis: Email headers contain a wealth of metadata that can be used to evaluate the authenticity of an email. With AI, spam filters can more effectively identify anomalies or trends that point to an email being part of a spam campaign.
Network Analysis: By building and analyzing a graph of email communication between different addresses, AI can more effectively identify anomalies or trends. For example, an email from an address that has never corresponded with the recipient in the past is more likely to be spam.

Spam Filtering with Harmony Email and Collaboration

Spam emails range from annoyance to significant security threats. While some are designed to advertise deals or promotions, others are intended to deliver malware to the recipient. In all cases, spam filtering is essential to keep employees’ inboxes clean and secure.

Check Point Harmony Email and Collaboration offers robust protection against email security threats, including the ability to leverage AI and other solutions to identify and block spam content. The 2023 Forrester Wave for Enterprise Email Security highlights how Harmony Email and Collaboration compares to the competition.