AI infers DoS mitigation rules
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F63839172%3A_____%2F22%3A10133497" target="_blank" >RIV/63839172:_____/22:10133497 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1007/s10844-022-00728-2" target="_blank" >https://doi.org/10.1007/s10844-022-00728-2</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/s10844-022-00728-2" target="_blank" >10.1007/s10844-022-00728-2</a>
Alternative languages
Result language
angličtina
Original language name
AI infers DoS mitigation rules
Original language description
DDoS attacks still represent a severe threat to network services. While there are more or less workable solutions to defend against these attacks, there is a significant space for further research regarding automation of reactions. In this article, we focus on one piece of the whole puzzle. We strive to automatically infer filtering rules which are specific to the current DoS attack to decrease the time to mitigation. We employ a machine learning technique to create a model of the traffic mix based on observing network traffic during the attack and normal period. The model is subsequently converted into the filtering rules. We evaluate our approach on several datasets. We experiment with various setups of hyperparameters as well as the various intensity of the attack traffic. The results of our experiments show that the proposed approach is feasible in terms of the capability of inferring successful filtering rules as well as inferring them in a reasonable time.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
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
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Continuities
R - Projekt Ramcoveho programu EK
Others
Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Journal of Intelligent Information Systems
ISSN
1573-7675
e-ISSN
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Volume of the periodical
2022
Issue of the periodical within the volume
23 August 2022
Country of publishing house
NL - THE KINGDOM OF THE NETHERLANDS
Number of pages
20
Pages from-to
1-19
UT code for WoS article
000843424300001
EID of the result in the Scopus database
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