The Role of Machine Learning in Cybersecurity

Data Science & Machine Lea

With an increasing reliance on online networks, cloud computing, and online data storage, companies must improve their cybersecurity practices. As the cyber terrain grows, so do the onslaught of cyber threats that put companies at risk of data breaches, loss of sensitive data, and other ever-evolving cyber threats. Organizations must transform their security posture, expand beyond perimeter-based security measures, and embrace new machine learning cybersecurity practices that enhance network security. 

Integrating machine learning in cybersecurity has proved crucial in improving a company’s security posture and, specifically, protecting the outer boundaries of a network. It adds an additional layer to security practices which will effectively protect organizations against cyber threats. Understanding the role of machine learning in cybersecurity will support organizations in creating strong, multi-layered security defenses to reduce or eliminate complex digital threats.  Let’s break down the benefits of machine learning in cybersecurity! 

What is Machine Learning? 

Before we really get into machine learning for cybersecurity, let’s clear up what machine learning is. In simple terms, machine learning is a subset of artificial intelligence (AI) that uses datasets to teach machines how to identify patterns, make predictions, and pull valuable insights from data. 

Essentially, machines develop algorithms and models without being explicitly programmed. Instead, they’re fed mass amounts of data and from which they identify trends, sort information, or take action based on data, rather than relying on specific programming instructions. Once the machine has learned from data sets, it’s able to make predictions regarding unseen or future data. 

Types of machine learning

There are three standard machine learning methods: 

Supervised learning: the process of training a model with data that is already labeled, meaning that data scientists know the desired outcomes and can train the machines to reach those same conclusions. After the algorithm learns by studying these examples, the machine can automatically make predictions when presented with new, unlabeled, or unknown data. 
Unsupervised learning: the opposite to supervised learning in that the data is unlabeled and the outcomes are unknown. The machine’s task is to discover patterns, trends, and similarities in data and group them together without knowing the desired outcome.
Reinforcement learning: trains an algorithm through giving the machine regular feedback. Positive outcomes are reinforced while undesired outcomes are punished, teaching the algorithm to identify certain trends in data, and adjust its decision-making strategy to enhance its performance over time. 

The chosen machine learning method is determined based on individual circumstances, the availability of data, and the desired outcomes. 

Applications of machine learning

Machine learning has proven useful in a number of fields. It’s rapidly advancing and has had a profound impact on a variety of industries, including: 

Healthcare
Finance 
Recommendation services
Computer vision 
Robotics
Manufacturing and product fulfillment

Thanks to the availability of large datasets, machine learning has been greatly improved in recent years and continues to be an innovative aspect of artificial intelligence. Familiarizing yourself with machine learning’s applications will prove useful for all tech professionals.