More than 300 billion emails are sent per day throughout the world, and about 50% are spam. It is the sender`s responsibility to filter spam to safeguard recipients. However, this filtering task is not easy, basically because classifying criteria between spam and ham messages are changing constantly. Developers have optimized the integration of ML (Machine Learning) to automate spam detection, so as to make the process more efficient. A common user will see that the bulk of messages in his junk box grows alongside that of his inbox, and this is thanks to the algorithms that filter out emails.
Spam messages can be categorized. Some are merely fake or upsetting emails with no bad intentions. Some others are phishing emails, i.e. they aim at making the receiver click on a link that will activate malware or even infect the device. There are less innocent ones, with the intention of deceiving and making the addressee reveal personal information, codes, passwords, etc., for fraudulent purchases or loans applications, for example. Basically spam detection analyses the content for classifying messages as unwanted or risky.
But not only content analysis is enough. ML generates algorithms to categorize messages by means of statistical models. These ML models are trained so as to spot words that are flagged to identify spam alerts. ML algorithms are effective in general, but Zyla Labs has devised an API with an algorithm that is based on past experience, so that it is constantly being trained, and the efficiency is constantly growing. Certain words and word sequences are the key to detect spam. This classification is possible thanks to NLP (Natural Language Processing) that is highly efficient when it is properly trained.
Every time you identify a message as “spam”, Zyla labels take this trained data and adjust the algorithm to spot a similar message to classify it as spam, though the supplier has its own ways to avoid excessive use of the “Report Spam” function. Specific data sets are generated for the particular field of activity of the client, so that the ML models are tailored to the specific vocabularies and language.
Statistical processing is the strategy to develop Spam Detection API. It is integrated with a suite of APIs for every special step (Anti-Spam Filter API, Block Spammers API, Prevent Spam API and others). The text data must be split into chunks before input to ML algorithms, whether it is to train models or further to make predictions on new information. This step is called “tokenization”. You can remove “stop words” (articles, prepositions, determiners) to ease down the process.
There are also other approaches: “stemming” and “lemmatization” that turn terms to their basic forms so that ML model is simplified. Unigrams, bigrams, trigrams or n-grams are one, two, three and n- word tokens are considered to simplify the identification process. Frequency of occurrence of words or word sets is measured to train detection for classifying emails as spam or ham. When the weight has been defined, the ML model is capable of filtering out spam.
Spam detection is practically 100% efficient. Sometimes it may be too fussy and classify an email as spam for the ambiguity of the language context, for example. This does not mean the process has been trapped, but on the contrary it is because before the doubt it sends the message to the junk box for the user to determine whether it is spam or ham.
The API is optimized by integrating recurrent neural networks as well as transformers that are highly effective as they process email and text messages as sequential data. Notice that spam detection is always at work. AI, ML and NLP are there to spot and filter messages, fishy senders, weird references and ambiguous texts in general. Nevertheless the API also relies on the user to improve and help detect the technique to optimize the process.