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- W3158335281 abstract "The paper discusses a technique for detecting cyberattacks on computer networks, based on identifying anomalies in network traffic by assessing its self-similarity and determining the impact of cyber attacks using statistical methods. The proposed technique includes three stages, at which the analysis of the self-similarity property for the reference traffic is performed (using the methods of the Dickey-Fuller test, rescaled range, and detrended fluctuation), the analysis of the self-similarity property for the real traffic (by the same methods) and additional processing of time series with statistical methods (methods of moving average, Z-Score, and CUSUM). The issues of software implementation of the proposed approach and the formation of a dataset containing network packets are considered. The experimental results demonstrated the presence of self-similarity in network traffic and confirmed the high efficiency of the proposed method. This technique allows detecting cyberattacks in real or near real time." @default.
- W3158335281 created "2021-05-10" @default.
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- W3158335281 date "2021-03-01" @default.
- W3158335281 modified "2023-09-26" @default.
- W3158335281 title "A technique for early detection of cyberattacks using the traffic self-similarity property and a statistical approach" @default.
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- W3158335281 doi "https://doi.org/10.1109/pdp52278.2021.00052" @default.
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