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A DDoS Attacks Detection Based on Conditional Heteroscedastic Time Series Models

Andrysiak T., Saganowski Ł., Maszewski M., Grad P.

Image Processing & Communications

2015

Vol. 20, no. 1

23--33

Dynamic development of various systems providing safety and protection to Network infrastructure from novel, unknown attacks is currently an intensively explored and developer domain. In the present article there is presented an attempt to redress the problem by variability estimation with the use of conditional variation. The predictions of this variability were based on the estimated conditional heteroscedastic statistical models ARCH, GARCH and FIGARCH. The method used for estimating the parameters of the exploited models was determined by calculating maximum likelihood function. With the use of compromise between conciseness of representation and the size of estimation error there has been selected as a sparingly parameterized form of models. In order to detect an attack-/anomaly in the network traffic there were used differences between the actual network traffic and the estimated model of the traffic. The presented research confirmed efficacy of the described method and cogency of the choice of statistical models.

Zagrożenia wirusowe w sieciach teleinformatycznych - wyniki badań

Staniec I., Jaszczura M.

Zeszyty Naukowe. Organizacja i Zarządzanie / Politechnika Łódzka

2007

z. 43

147-155

Celem przedstawionych rozważań jest scharakteryzowanie słabych i mocnych stron polityki bezpieczeństwa informacji oraz analiza statystyczna zagrożeń wirusowych pojawiających się w sieciach teleinformatycznych.

The aim in this paper is present pattern poor and strong insurance policy in network. It`s presents criticize insurance engineering and tools use to protection network and statistical analysis about virus infect.