Email has retained a central position in the bulk of communication tools. Together with its popularity that has not been affected by chat apps, the risk of spam has grown exponentially. Users have acquired experience in detecting spam email. They are very attentive to mysterious senders, attractive and too generous offers, mistakes in the text, dubious links, and even vulgar language. But spammers do their best to make their messages look innocent and real, and many users fall into the trap.
Despite the careful attention, users need a strong protection. Developers use ML and knowledge engineering to classify emails in two categories: spam or ham. The filter software scans messages through a checklist, but this is not 100% guarantee as this checking tool must be constantly updated. This means that not any platform will render efficient, easy-use and fast service.
Today machines are trained to learn like humans. We can see how a car without a driver can drive itself, and we take for granted that many messages we receive go straight to the “Spam” folder. This is all thanks to Machine Learning. The software generates an algorithm that classifies rules from messages in the inbox. These algorithms are in use for filtering spam messages.
Zyla Labs has developed Spam Detection API to identify and divert spam emails or messages with spam content. It uses a combination of ML techniques that work as spam filters, and a suite of applications for a more accurate, efficient and faster solution (Anti-spam Filter API, Spam checker API, Spam prevention API, etc.). This successful ensemble of software uses already existing rules and generates the new necessary ones to accomplish its work with precision.
To cope with the constant attacks of spam emails, the process is boosted by a machine learning model, by means of supervised learning to train the machine with tagged data with the correct answer. That supervised learning generates an algorithm that analyses trained data and produces the expected output. The machine collects unclassified data by similar and different patterns in the case it uses unsupervised learning.
The application classifies words, which is a widespread approach to filter mail. There is probability that certain words will appear in spam mail or in ham mails. If the whole number of probabilities trespasses a given limit, the filter is activated to categorize the email. Another resource of these APIs is Neural Network, i.e. a network of artificial neurons that varies its structure depending on the data that flows during a learning segment.
Accuracy comes from the integration of ML and deep learning. This allows machines to learn empirically, assimilating valuable patterns from more rudimentary information. To put the application to the test, some sets of spam and ham emails must be compiled; they are submitted to analysis and it is possible to get the level of accuracy and security of the API.
We are today under the threat of spam in this era of wide communication and advanced technology. This is an inherent part of email. Email popularity means that there is a black cloud that covers all the global community of email users. But there comes the solution: a security system of filtering and detecting spam emails to an error rate of 0.01%, which means that only one out of one thousand emails does not come spam filtered.