Statistical Properties of DDoS Attacks

Erhan, Derya; Anarım, Emin

doi:10.1109/codit.2019.8820574

Cited by 4 publications

(2 citation statements)

References 19 publications

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“…Among the main ideas is the monitoring of changes in the probability distribution of the occurrence of packets during the transition from standard traffic to attack. For this reason, the statistical moments describing the distribution of packet occurrences will change, for example, average rate, variance, spiciness coefficient [9], measures of periodicity, kurtosis, skewness, and selfsimilarity [10,11].…”

Section: Related Workmentioning

confidence: 99%

Machine Recognition of DDoS Attacks Using Statistical Parameters

Smiesko,

Segec,

Kontsek

2023

Mathematics

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As part of the research in the recently ended project SANET II, we were trying to create a new machine-learning system without a teacher. This system was designed to recognize DDoS attacks in real time, based on adaptation to real-time arbitrary traffic and with the ability to be embedded into the hardware implementation of network probes. The reason for considering this goal was our hands-on experience with the high-speed SANET network, which interconnects Slovak universities and high schools and also provides a connection to the Internet. Similar to any other public-facing infrastructure, it is often the target of DDoS attacks. In this article, we are extending our previous research, mainly by dealing with the use of various statistical parameters for DDoS attack detection. We tested the coefficients of Variation, Kurtosis, Skewness, Autoregression, Correlation, Hurst exponent, and Kullback–Leibler Divergence estimates on traffic captures of different types of DDoS attacks. For early machine recognition of the attack, we have proposed several detection functions that use the response of the investigated statistical parameters to the start of a DDoS attack. The proposed detection methods are easily implementable for monitoring actual IP traffic.

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Section: Related Workmentioning

confidence: 99%

Machine Recognition of DDoS Attacks Using Statistical Parameters

Smiesko,

Segec,

Kontsek

2023

Mathematics

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“…The work in [6] used four measures of periodicity, the kurtosis, skewness and self-similarity of a time series to compare histograms of the number of packets during normal and attack traffic. The authors in [7] used the fitting of the probability distribution of the monitored IP traffic with continuous distributions using three statistical moments and then tested the hypotheses about the occurrence of a DDoS attack. Using the change of autocorrelation at the onset of a DDoS attack was studies by, e.g., [8].…”

Section: Related Workmentioning

confidence: 99%

One-Parameter Statistical Methods to Recognize DDoS Attacks

et al. 2022

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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.

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Statistical Properties and Modelling of DDoS Attacks

Machaka

Bagula

2021

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Statistical Properties of DDoS Attacks

Cited by 4 publications

References 19 publications

Machine Recognition of DDoS Attacks Using Statistical Parameters

Machine Recognition of DDoS Attacks Using Statistical Parameters

One-Parameter Statistical Methods to Recognize DDoS Attacks

Statistical Properties and Modelling of DDoS Attacks

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