A QUANTUM ALGORITHM TO LOCATE UNKNOWN HASHES FOR KNOWN N-GRAMS WITHIN A LARGE MALWARE CORPUS
Nicholas R. Allgood, Charles Nicholas · arXiv (Cornell University) · 2020
Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields. Malware analysis is one of those fields that could also take advantage of quantum computing. The combination of software used to locate the most frequent hashes and n-grams between benign and malicious software (KiloGram) and a quantum search algorithm could be beneficial, by loading the table of hashes and n-grams into a quantum computer, and thereby speeding up the process of mapping n-grams to their hashes. The first phase will be to use KiloGram to find the top-k hashes and n-grams for a large malware corpus. From here, the resulting hash table is then loaded into a quantum machine. A quantum search algorithm is then used search among every permutation of the entangled key and value pairs to find the desired hash value. This prevents one from having to re-compute hashes for a set of n-grams, which can take on average O(MN) time, whereas the quantum algorithm could take O(√N) in the number of table lookups to find the desired hash values.