Discrete Finite Automata Graph Traversal for Malware Detection

The patent segments the search space by organizing processes and files into a graph data structure with hierarchical relationships. The discrete finite automaton divides the traversal into targeted paths based on malware patterns, rather than exhaustively searching all processes and files. This segmentation allows the system to focus computational resources on relevant portions of the graph, significantly reducing search time while maintaining detection accuracy.

The patent performs preliminary actions by pre-compiling malware detection patterns into a discrete finite automaton before the actual search. The automaton is constructed in advance with predefined states and transitions that represent known malware behaviors and characteristics. This preliminary preparation enables the search algorithm to efficiently match observed processes and files against pre-defined malware patterns without requiring complex runtime analysis, thus reducing search time while preserving reliability.

The graph data structure segments the computational task into manageable units representing processes, files, and their relationships. The discrete finite automaton further segments the traversal by following only those paths that match malware patterns, avoiding exhaustive exploration of the entire graph. This segmented approach reduces computational resource consumption by focusing processing power on relevant subgraphs rather than the complete process and file space.

The discrete finite automaton serves as an intermediary between the graph data structure and the malware detection logic. Instead of directly analyzing each process and file against complex malware signatures, the automaton mediates the search by translating graph traversals into pattern matches. This intermediary layer simplifies the computational burden by pre-structuring the detection logic, thereby reducing energy consumption while maintaining detection accuracy.