Data Package Classification via Normalization and Permutation

The patent applies preliminary action by performing normalization on data packages before security analysis. The normalization process standardizes various malicious data representations into a common format, creating a unified search term that can be efficiently checked against security databases. This preprocessing step reduces the complexity of subsequent security checks while maintaining thorough detection capabilities.

The patent employs parameter changes by transforming the data package through normalization and permutation operations. By changing the representation parameters of malicious data into standardized forms, the system enables more efficient pattern matching and classification. The data model applies various transformations to generate permutations that are then classified using machine learning models, improving both speed and accuracy.

The patent applies merging by combining multiple different potentially malicious data representations into a single normalized form. The normalization process consolidates various obfuscation techniques, character variations, and encoding methods into a unified search term. This allows the system to detect multiple malicious patterns using a single classification operation, significantly reducing processing requirements while maintaining comprehensive detection accuracy.

The patent implements universality through the normalization framework that handles multiple types of malicious data representations with a single unified approach. The data model and machine learning classifiers are designed to work with normalized data regardless of the original representation, enabling the system to detect phishing attempts, obfuscated malware, and other threats through a universal classification mechanism rather than requiring separate detection systems for each threat type.