While the features computed and organized by the Aegis CyberXtractor™ are valuable, they are not yet optimal inputs for training accurate AI anomaly detection models. They suffer from certain limitations that need to be addressed. Firstly, they result in large-scale sparse matrices due to the presence of numerous computed features with missing or zero indicators when not applicable. These matrices require special mathematical dimension reduction techniques. Additionally, these features may not exhibit sufficient cross-correlation, and it is crucial to compute more optimal features from them that are better suited for AI modeling.
Aegis CyberEngineer™ toolbox greatly facilitates and accelerates AI model building by providing a no-coding approach to mathematical feature engineering. With its intuitive web dashboard, users can effortlessly perform complex mathematical operations through a simple drag-and-drop interface. This platform eliminates the need for scripting or extensive mathematical background, making it accessible to data scientists and users of all skill levels. They can focus on the core aspects of their work without being hindered by technical complexities, and save time and effort in the model development lifecycle.
The Aegis CyberEngineer™ is a high-performance, no-coding data processing microservice designed to empower data science engineers. With its intuitive web dashboard, users can effortlessly drag and drop a sequence of mathematical processors from a pre-established library onto any set of base features. This powerful tool offers a range of functionalities: