Decision Tree-Based Ensembles To Specify Secret Keys For Cellular Automata Cryptographic Model

Abstract

A cryptographic method based on cellular automata (CA) was previously proposed which employs transition rules as secret keys. However, some rules belonging to the possible key space present undesirable behaviors that must be avoided. In a previous work, it was investigated the secret key specification for this cryptography model associating rules performance in ciphering with CA static parameters. A genetic algorithm-based data mining was performed to discover adequate key specification and it was employed to filter the set of all possible radius 2 CA rules. It was able to discover good secret key specifications. However, such filter provokes a significant decay in the number of good keys, while still keeping some underperforming rules. Adequate secret key specifications are investigated here using decision tree ensembles: bootstrap aggregating (bagging), boosting and random forest. The new filters are compared to the previous ones. By applying the new methodology, it was possible to find filters able to eliminate almost all underperforming rules and keeping a higher number of adequate secrete keys.