Persistence homology – filtering noise to find meaningful patterns
TDA and its method of persistent homology provide a groundbreaking approach to cyber threat detection that complements traditional techniques. In a world where threats are constantly evolving and new malware is being developed, the ability to identify and classify potentially harmful software based on its inherent data structure is invaluable.
To better understand the usefulness of this approach, let’s dig deeper into how persistence diagrams, the graphical representation of topological data, can be leveraged to identify benign software and detect novel threats.
As we explained earlier, benign software, when analyzed using persistent homology, typically presents a predictable structure. This might mean tight clusters of data points representing common or routine software activities, fewer loops indicating less intricate interactions, and simpler connections that align with the software’s legitimate...