How Spam Filtering Techniques Are Adopting The Latest Technologies Such As Machine Learning
Inception and advancements of technology come with many risks of online threats. Many threats became obsolete, but spam is one threat that doesn’t seem to be going away any time soon. Spam emails are not always about peculiar ads; instead, they can also be emails containing malicious attachments, targeting your organization’s network. For instance, spam can infect your system with ransomware, causing severe consequences as threat actors lock your data and demand ransom in return to unlock it. All these make implementing proper techniques of spam filtering essential in today’s times.
What Is A Spam Filter And How Does It Work?
A spam filter is a program developed to spot unwanted, suspicious email messages. Thus, it prevents such unsolicited messages from reaching a user’s email inbox.
What do spam filters look for are specific spammy criteria that it can impose on incoming emails. As a result, it generates a score based on which an email will be considered legit or spam. When it comes to the best free spam filters to secure the systems, various options are available in the market.
Using Machine Learning for Email Spam filtering
The machine learning technique uses statistical methods for automatic email classification to filter spam from a user’s inbox. Some popular Machine Learning techniques for spam filtering are Naive Bayes, Support Vector Machines, Decision Trees, Neural Networks, etc. The sophistication of Machine learning algorithms makes it one of the best spam filter services among all other spam filtering techniques. The success of spam filter Gmail can be ascribed to Gmail’s timely transition and successful use of the Machine Learning techniques to filter both, incoming spam and other abuses like Denial-of-Service (DoS) attacks. The reason for the success of Machine Learning based spam filters is that they retrain themselves when put in use and minimize the manual effort required while delivering superior filtering accuracy.
Which Are Other Popular Spam Filtering Techniques?
- Honey pots: A honeypot is a decoy system or server set up to gather information about hackers or collect spam. It offers content-based spam filtering by using a fingerprint-based technique. They help security professionals to learn about the latest techniques used by hackers.
- Signature Schemes: These schemes are used by most antivirus products, which work on the basis of signatures. The MTAs store the hashes of spam messages identified previously and check all the incoming mails against these signatures. Since signatures match the patterns exactly, these spam filtering solutions can detect known intrusions easily.
- Collaborative Spam Filtering: It is a distributed approach to filter spam. A whole community works with a piece of shared information about spam and develops techniques to filter out the same.
This article saw how Machine Learning and other Spam filtering techniques are extremely beneficial to stay safe from unsolicited emails. However, a single technique cannot provide 100% protection against spam. Hence, an organization needs to search for a solution with multiple spam-filtering techniques to better deal with junk emails. However, staff training is also necessary to combat this situation and streamline the spam filtering process.
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