Machine learning (ML) is a method of data and information analysis that allows machines to learn and evolve. ML is one of the mechanisms behind artificial intelligence (AI). Its goal is to develop an AI system so that the machine makes decisions on its own, always learning and evolving, with as little human interaction as possible.
How machine learning works
Machine learning works based on algorithms. But what are algorithms? An algorithm is a set of instructions and guidelines that must be followed in order for an objective to be achieved. We heard once: “think about an algorithm as a recipe”. It’s easier to see it that way.
Thus, in practice, ML uses algorithms to analyze large volumes of data and information in search of patterns and anomalies that can act as a trigger for a specific action and can still be used for predictions.
There are different types of algorithms and they can be used in different areas. To mention a few, here at Gatefy, we use algorithms to identify spam, phishing, malware and suspicious emails. YouTube and Spotify use algorithms to recommend videos and music. Uber and Waze use algorithms to choose better routes.
Types of machine learning algorithms
Although there are many types of machine learning algorithms, we can separate them into three main categories that vary according to the method and how the machines learn.
1. Supervised learning
Supervised machine learning is a type of algorithm that works with examples and labels. The goal is to teach the machine to learn to label new information according to the established examples and the feedback it receives, checking whether it has made the right decision or not.
2. Unsupervised learning
Unsupervised machine learning doesn't work with examples or feedback. The machine is taught to understand and interpret information so that it can organize them rationally so that they are understandable. In other words, the machine automatically creates patterns and relationships between the data without the interference of a person.
3. Reinforcement learning
A machine learning algorithm based on reinforcement allows the machine to make a decision according to the context. It's a type of learning that takes into account the behavior of the machine, which receives feedback on its hits and misses. Over time, the machine learns to make better decisions respecting specific contexts.
Machine Learning and cybersecurity
For us who work with email security, it's almost impossible to talk about machine learning and not talk about cybersecurity. These are concepts that have been connected for some decades and can no longer be dissociated.
Algorithms play a decisive role in fighting threats, especially if we are talking about advanced and unknown threats, or also called zero-day attacks. The ability to learn makes AI and ML systems indispensable today, even more if we take into account that thousands of threats are created daily.
In the case of Gatefy, machine learning algorithms are a powerful weapon in analyzing and predicting suspicious and malicious behaviors in emails, with the ability to detect spam, malware, and other attacks. Remembering that email continues to be the main vector of attacks and threats on the internet and that machine learning should be thought of not as a complete solution but as an important part of a security system that takes into account different protection mechanisms.
If you have any questions, write to us at firstname.lastname@example.org.