If you wonder how machine turns speech into written text, or how voice assistants respond to your questions, or how messages are classified as spam or ham, the answer is “Machine Learning”, which is software integrated to automate processes. The exponential growth in the volume of unwanted emails has demanded the evolution of anti-spam filters. ML adds efficiency to spam detection, by automating the process through the generation of algorithms to scan text.
The struggle against spam with the use of ML techniques is constantly growing in sophistication and sensitivity. ML has paved the way for deep learning and deep adversarial learning, which are introduced as the future techniques for dealing with the threat of spam emails. ML classifies emails as spam or ham, i.e. binary classification. The detection is then simple and effective, and prevents undesired messages from invading the user`s inbox.
The task of ML in APIs is the identification of email as “spam”, considering that spam email is assigned value “1” and all the others are assigned value “0”, i.e. the target variable (1 or 0) is exactly the expected prediction. This target value is constantly trained and also predicted by other variables. The process includes four aspects for text classification: text processing, text sequencing, model selection and implementation.
The first step consists of skimming text data to get what is essential for analysis (eliminating superfluous words, special characters, stop words, numbers, white spaces and reducing all the raw text to lower case) , and then tokenizing the resulting text (splitting it into chunks or tokens as input to ML algorithm). Then follows text sequencing, which is the analysis of text streams, time-series data and all types of sequential text to sequence models to classify the text. The third step is that of model selection, which consists of choosing the most suitable model for the dataset, so as to train the predictive model against which accuracy of the text will be assessed; this model can forecast the variables of the dataset. Finally, the process can automate the prediction of outcomes based on the algorithm that uses previous data as input.
The threat of spam email is growing constantly, having reached a percentage of nearly 80% of responsibility of all the email traffic in the world. This of course impacts on financial losses, fraud, personal information leak, and virus spread. Although the common user has acquired expertise in spotting spam, there are still many messages that deceive the recipient, which is the main reason why we need a strong and efficient security program.
APIs have evolved with the integration of Machine Learning for spam filtering. A set of rules is created that constitutes a method without the necessity of constant updating. This is a subfield of AI (Artificial Intelligence) to make machines learn as if they were human. All unwanted or suspect mail goes to a spam folder by effect of ML integrated in applications (Anti Spam Filter API, Block Spammers API, Prevent Spam API, Spam Prevention API, etc.).
Spam Detection API using ML needs training examples to operate, which are properly classified for the algorithm to apply the classification rules. The various combinations of ML techniques are employed in the specific APIs. They can adapt to varying conditions, and their effect goes beyond the task of only checking junk emails. They are storing findings to generate new rules for the spam filtering mission. ML works grouping random information, classifying it by similarities, repeated patterns and differences, thus building up models against which messages are assessed to detect spam.