Mathematical Frameworks for Threat Intelligence Analysis: Leveraging Graph Theory and Machine Learning for Cyber Threat Assessment

Abstract

Because there are so many complex online risks, we need new ways to look at threat data. This study suggests a complete approach that combines graph theory and machine learning methods to make figuring out cyber threats better. The basic idea behind networks is graph theory, which lets us show the complicated connections between different things in a connected world. This approach gives a full picture of the danger scene by representing cyber entities and how they interact as nodes and lines in a graph. This makes it easier to spot trends and outliers. The system includes machine learning techniques that make use of the huge amount of data that is available for analyzing cyber threats. Supervised learning methods are used for classification tasks. These let threats be put into groups based on past data and known patterns of bad behavior. Unsupervised learning methods, on the other hand, make finding anomalies easier by noticing changes in how networks normally behave. These machine learning models learn to adapt to changing threats by being trained and improved over and over again. This makes methods for finding threats and stopping them more effective. Combining graph theory and machine learning makes it possible to get useful information from a huge number of different data sources. Graph-based analytics bring together different kinds of data, like network traffic, system logs, and threat intelligence feeds, into a single view. This helps you see the connections between things that don't seem to be related. Machine learning algorithms improve this analysis by finding small patterns and trends that point to bad behavior. This gives cybersecurity professionals the power to stop new threats before they happen. Scalability and freedom are built into the suggested system so it can adapt to changing cyber dangers and network platforms. It can handle big datasets and real-time streaming data well by using distributed computer structures and flexible machine learning methods. This makes sure that threats are found and dealt with quickly. Putting graph theory and machine learning together is a good way to make threat intelligence research better in defense.