Catch Me if You Can: Improving Adversaries in Cyber-Security with Q-Learning Algorithms
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F23%3A00364619" target="_blank" >RIV/68407700:21230/23:00364619 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.5220/0011684500003393" target="_blank" >https://doi.org/10.5220/0011684500003393</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.5220/0011684500003393" target="_blank" >10.5220/0011684500003393</a>
Alternative languages
Result language
angličtina
Original language name
Catch Me if You Can: Improving Adversaries in Cyber-Security with Q-Learning Algorithms
Original language description
The ongoing rise in cyberattacks and the lack of skilled professionals in the cybersecurity domain to combat these attacks show the need for automated tools capable of detecting an attack with good performance. Attackers disguise their actions and launch attacks that consist of multiple actions, which are difficult to detect. Therefore, improving defensive tools requires their calibration against a well-trained attacker. In this work, we propose a model of an attacking agent and environment and evaluate its performance using basic Q-Learning, Naive Q-learning, and DoubleQ-Learning, all of which are variants of Q-Learning. The attacking agent is trained with the goal of exfiltrating data whereby all the hosts in the network have a non-zero detection probability. Results show that the DoubleQ-Learning agent has the best overall performance rate by successfully achieving the goal in 70% of the interactions.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 15th International Conference on Agents and Artificial Intelligence
ISBN
978-989-758-623-1
ISSN
2184-3589
e-ISSN
2184-433X
Number of pages
8
Pages from-to
442-449
Publisher name
SCITEPRESS – Science and Technology Publications, Lda
Place of publication
Lisboa
Event location
Lisbon
Event date
Feb 22, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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