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Russia Moscow
Section Computer science
Title Detecting DDoS attacks by analyzing the dynamics and interrelation of network traffic characteristics
Author(-s) Krasnov A.E.a, Nadezhdin E.N.a, Nikol'skii D.N.a, Repin D.S.a, Galyaev V.S.a
Affiliations State Institute of Information Technologies and Telecommunicationsa
Abstract This paper presents an improved approach previously developed by the authors for detection of DDoS attacks. It uses traffic evolution and dynamical operators, which makes it possible to take into consideration interrelations observed for data packets headers of traffic. It is assumed that each traffic state (normal state and anomalous attacked states) can be described by unique temporal patterns of characteristics generated by unknown linear dynamical operators. Interrelations between values of network traffic characteristics in different discrete time samples are determined by the evolution operator. The approach was applied for classification of three traffic states: normal and two abnormal (HTTP flood and SlowLoris DDoS attacks). The results prove that it is possible to distinguish normal and abnormal traffic states by hash functions of address and load fields of traffic data packets.
Keywords network traffic, DDoS attack, detection, dynamical operator, evolution operator, hash function, classification
UDC 517.28, 530.181
MSC 90B20, 47A62
DOI 10.20537/vm180310
Received 15 June 2018
Language Russian
Citation Krasnov A.E., Nadezhdin E.N., Nikol'skii D.N., Repin D.S., Galyaev V.S. Detecting DDoS attacks by analyzing the dynamics and interrelation of network traffic characteristics, Vestnik Udmurtskogo Universiteta. Matematika. Mekhanika. Komp'yuternye Nauki, 2018, vol. 28, issue 3, pp. 407-418.
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