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Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection

Department of Electrical and Electronics Engineering, Boğaziçi University, Istanbul 34342, Turkey
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Presented at the International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), Kos Island, Greece, 2 September 2017.
Proceedings 2018, 2(2), 96; https://doi.org/10.3390/proceedings2020096
Published: 10 January 2018
Data availability should be guaranteed by a web service in order to satisfy customers. One of the main challenges of information security professionals is DDoS attack which affects the availability. By masquerading itself as a legitimate user, a DDoS attacker tries to overwhelm a server by sending a great number of useless packets that influences the quality of service (QoS) of the network. DDoS attack can result in a great damage to network services. Useless packets similar to normal ones are dispatched by the attacker which leaves the intrusion detection system impotent of detection. Transferring from conventional packet-based analysis methods to time series based (flow-based) algorithms would be a promising alternative to spot DDoS attacks. In this work, we extract four measures of periodicity, kurtosis, skewness and self-similarity of a time series and investigate the performance of these parameters in separating DDoS attack from normal traffic.
Keywords: DDoS; time series; kurtosis; skewness; hurst parameter DDoS; time series; kurtosis; skewness; hurst parameter
MDPI and ACS Style

Fouladi, R.F.; Kayatas, C.E.; Anarim, E. Statistical Measures: Promising Features for Time Series Based DDoS Attack Detection. Proceedings 2018, 2, 96.

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