One-Parameter Statistical Methods to Recognize DDoS Attacks

Within our academic high-speed network infrastructure which is used for connecting all universities and high schools in our country to the Internet, there are thousands of cybersecurity attacks occurring every day. That is why, within our SANET II project, an effort has been made to create a self-le...

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Bibliographic Details
Published in:Symmetry (Basel) 2022-11, Vol.14 (11), p.2388
Main Authors: Hajtmanek, Roman, Kontšek, Martin, Smieško, Juraj, Uramová, Jana
Format: Article
Language:English
Subjects:
Algorithms
anomaly
Bandwidths
Coefficient of variation
Colleges & universities
Cybersecurity
Data encryption
datasets
DDoS attack
Denial of service attacks
detection
Entropy
Fractals
High speed
IP traffic
Machine learning
Probability distribution
Recognition
Servers
Statistical methods
Time series
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Summary:Within our academic high-speed network infrastructure which is used for connecting all universities and high schools in our country to the Internet, there are thousands of cybersecurity attacks occurring every day. That is why, within our SANET II project, an effort has been made to create a self-learning system without a teacher, which would be able to quickly adapt to arbitrary traffic and recognize DDoS attacks on time, even in high-speed networks, with a potential simple implementation into a hardware probe. In the article, we deal with the Hurst and autoregression coefficients and the coefficient of variation. We test the coefficients on simulated data and on real records of attacks. For early machine recognition of the attack, we propose the so-called predicting σ-tunnel. The obtained results can lead to the investigation of other prediction methods that would improve the early recognition of an attack.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym14112388