Overview
The client began experiencing an increase in the volume of spam through its estimate request form. Unfortunately, a CAPTCHA did not solve the problem, and neither did fancy tricks with the contact form itself.
Web forms, such as our clients estimate request form, are particularly vulnerable because they are often not secure, making it easy for spammers to access them without any authentication or verification. Additionally, many web forms are not set up to prevent spam, leaving them open to abuse. Many spammers also use automated tools to fill out web forms, making it easier for them to send out large amounts of spam without detection.
Moreover, spammers are growing more sophisticated. As technology continues to advance, so do the tactics used by spammers to get around security measures. Spammers are now capable of bypassing even the most advanced spam filters, making it more difficult to detect and stop the spread of unsolicited and malicious spam.
Approach
Utilizing old data, we determined we could go through the clients request history and flag the spam, then use machine learning to train a simple model to classify estimate requests as spam or not spam.
Machine learning is an artificial intelligence technology that enables computers to learn from data and make predictions or decisions based on that data. It is a branch of artificial intelligence that allows computers to learn by themselves, without being explicitly programmed. Machine learning algorithms can be divided into two main categories: supervised learning and unsupervised learning. Supervised learning algorithms use labeled data to make predictions or decisions, while unsupervised learning algorithms use unlabeled data to uncover patterns and insights.
Results
Our approach is 99% accurate in identifying spam messages and after integrating it into their request form, they are no longer inundated with spam. Our client is thoroughly pleased with the outcome of this project.
We utilized machine learning to identify and prevent spam. By analyzing the data associated with a form, the system was trained to recognize common features of spam, allowing it to filter out unwanted forms before they are sent.
Conclusion
We were able, with 99% accuracy filter out unwanted spam from the clients estimate request form utilizing ML technology. Machine learning is a powerful tool and in this case, used to identify spam with web forms. This technique uses algorithms to analyze data and identify patterns, which can then be used to detect and prevent spam. With machine learning, the system can be trained to recognize common features of spam, such as email addresses, URLs, and IP addresses. Once trained, the system can then be used to filter out unwanted forms, preventing them from reaching the intended recipient.