Publication detail

Distributed password cracking with BOINC and hashcat

HRANICKÝ, R. ZOBAL, L. RYŠAVÝ, O. KOLÁŘ, D.

Original Title

Distributed password cracking with BOINC and hashcat

English Title

Distributed password cracking with BOINC and hashcat

Type

journal article in Web of Science

Language

en

Original Abstract

Considering today's challenges in digital forensics, for password cracking, distributed computing is a necessity. If we limit the selection of password-cracking tools strictly to open-source software, hashcat tool unambiguously wins in speed, repertory of supported hash formats, updates, and community support. Though hashcat itself is by design a single-machine solution, its interface makes it possible to use the tool as a base construction block of a larger distributed system. Creating a "distributed hashcat" which supports the maximum of hashcat's original features requires a smart controller that employs different distribution strategies in different cases. In the paper, we show how to use BOINC framework to control a network of hashcat-equipped nodes and provide a working solution for performing different cracking attacks. We also provide experimental results of multiple cracking tasks to demonstrate the applicability of our approach. Last but not least, we compare our solution to an existing hashcat-based distributed tool - Hashtopolis.

English abstract

Considering today's challenges in digital forensics, for password cracking, distributed computing is a necessity. If we limit the selection of password-cracking tools strictly to open-source software, hashcat tool unambiguously wins in speed, repertory of supported hash formats, updates, and community support. Though hashcat itself is by design a single-machine solution, its interface makes it possible to use the tool as a base construction block of a larger distributed system. Creating a "distributed hashcat" which supports the maximum of hashcat's original features requires a smart controller that employs different distribution strategies in different cases. In the paper, we show how to use BOINC framework to control a network of hashcat-equipped nodes and provide a working solution for performing different cracking attacks. We also provide experimental results of multiple cracking tasks to demonstrate the applicability of our approach. Last but not least, we compare our solution to an existing hashcat-based distributed tool - Hashtopolis.

Keywords

hashcat, BOINC, cracking, distributed computing, GPGPU

Released

10.04.2019

Publisher

NEUVEDEN

Location

NEUVEDEN

Pages from

161

Pages to

172

Pages count

11

URL

Documents

BibTex


@article{BUT159967,
  author="Radek {Hranický} and Lukáš {Zobal} and Ondřej {Ryšavý} and Dušan {Kolář}",
  title="Distributed password cracking with BOINC and hashcat",
  annote="Considering today's challenges in digital forensics, for password cracking,
distributed computing is a necessity. If we limit the selection of
password-cracking tools strictly to open-source software, hashcat tool
unambiguously wins in speed, repertory of supported hash formats, updates, and
community support. Though hashcat itself is by design a single-machine solution,
its interface makes it possible to use the tool as a base construction block of
a larger distributed system. Creating a "distributed hashcat" which supports the
maximum of hashcat's original features requires a smart controller that employs
different distribution strategies in different cases. In the paper, we show how
to use BOINC framework to control a network of hashcat-equipped nodes and provide
a working solution for performing different cracking attacks. We also provide
experimental results of multiple cracking tasks to demonstrate the applicability
of our approach. Last but not least, we compare our solution to an existing
hashcat-based distributed tool - Hashtopolis.",
  address="NEUVEDEN",
  chapter="159967",
  doi="10.1016/j.diin.2019.08.001",
  edition="NEUVEDEN",
  howpublished="print",
  institution="NEUVEDEN",
  number="1",
  volume="30",
  year="2019",
  month="april",
  pages="161--172",
  publisher="NEUVEDEN",
  type="journal article in Web of Science"
}