A Quantum Algorithm to Locate Unknown Hashgrams

Thumbnail Image

Files

2005.02911.pdf (248.74 KB)

Links to Files

https://link.springer.com/chapter/10.1007/978-3-031-18344-7_18

Permanent Link

https://doi.org/10.1007/978-3-031-18344-7_18
 http://hdl.handle.net/11603/26276

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

Author/Creator

Author/Creator ORCID

https://orcid.org/0000-0001-9494-7139

Date

2022-10-14

Type of Work

conference papers and proceedings
book chapters
preprints
Text

Department

Program

Citation of Original Publication

Allgood, N.R., Nicholas, C.K. (2023). A Quantum Algorithm to Locate Unknown Hashgrams. In: Arai, K. (eds) Proceedings of the Future Technologies Conference (FTC) 2022, Volume 3. FTC 2022 2022. Lecture Notes in Networks and Systems, vol 561. Springer, Cham. https://doi.org/10.1007/978-3-031-18344-7_18

Rights

This item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.

Subjects

Abstract

Quantum computing has evolved quickly in recent years and is showing significant benefits in a variety of fields, especially in the realm of cybersecurity. The combination of software used to locate the most frequent hashes and n-grams that identify malicious software could greatly benefit from a quantum algorithm. By loading the table of hashes and n-grams into a quantum computer we can speed 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 simulator. 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.