{"id":75968,"date":"2024-03-04T12:49:50","date_gmt":"2024-03-04T04:49:50","guid":{"rendered":"https:\/\/version-2.com\/?p=75968"},"modified":"2024-02-28T14:51:07","modified_gmt":"2024-02-28T06:51:07","slug":"the-role-of-machine-learning-in-cybersecurity","status":"publish","type":"post","link":"https:\/\/version-2.com\/en\/2024\/03\/the-role-of-machine-learning-in-cybersecurity\/","title":{"rendered":"The role of machine learning in cybersecurity"},"content":{"rendered":"
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Humans simply can no longer tackle the exponential growth of sophisticated online security threats in a timely and effective manner. Hence, automating cybersecurity processes with artificial intelligence (AI) and machine learning (ML) powered systems becomes vital.\u00a0<\/span><\/p><\/div>

So, does that mean IT teams will become redundant soon, as AI-based security tools can do it all? Simply put, no. But for a more in-depth answer, we’ll need to first understand what machine learning in cybersecurity is and what this technology holds for businesses in the future.<\/p>

What is machine learning?<\/h2>

Machine learning refers to the ability of algorithms to learn patterns from existing data and use this knowledge to predict outcomes on new, previously unknown data without explicitly being programmed. The more information you feed to the machine learning engine, the more data it can analyze and, consequently, become more accurate.<\/p>

But what does it mean to say that a machine is learning from the existing data? While traditional programming performs simple and predictable tasks by strictly following detailed instructions, machine learning allows the computer to teach itself through experience. In other words, it mimics human behavior in how to solve problems.<\/p>

However, the fact that machine learning can improve itself isn’t the only reason why it’s so easy to find its models in the online wilderness. The sheer amount of information that businesses in different industries currently have to manage has become too vast for humans to tackle alone. As a result, companies rely on machine learning to process that data and quickly generate actionable insights.<\/p>

For instance, an ML technique called a decision tree solves classification dilemmas and uses certain conditions or rules in the decision-making process. This particular technique is widely used in fintech (for loan approval and credit scoring) and marketing.<\/p>

Machine learning solutions are also helpful for businesses in harvesting, organizing, and analyzing large volumes of customer data. This can include purchasing history or individual customer’s typical behavior, such as online browsing habits. With such analyzed data, companies can then recommend relevant products tailored to their customers’ preferences. Think Netflix: With an ML-driven model, it examines its users’ histories on the platform to compile appropriate content recommendations for them to choose from. This increases the time users spend watching Netflix content and their overall satisfaction. Similarly, ML models pick up information relevant to the unique user on the Facebook feed and even moderate content on Instagram<\/a>.<\/p>

In addition, in most customer support self-service tools, users usually interact with a machine rather than a fellow human being. Such chatbots can answer basic questions and guide a person to relevant content on the website.<\/p>

Lastly, even in the medical field, machine learning plays a huge role. These models can be trained to examine medical images or other information and then search for illness characteristics.<\/p>

Four types of machine learning<\/h2>

Machine learning traditionally has four broad subcategories that are defined by how the machine learns:<\/p>