Modified Algorithm for Detecting Network Attacks Using the Fractal Dimension Jump Estimation Method in Online Mode

Sheluhin, Oleg I.; Rybakov, S. Y.; Vanyushina, A. V.

doi:10.31854/1813-324x-2022-8-3-117-126

Cited by 2 publications

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References 14 publications

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“…В работах [8][9][10] было показано, что на основе показателя Херста можно обнаружить аномальную активность сетевого трафика, которая может характеризоваться следующими статистическими характеристиками:…”

Section: постановка задачиunclassified

“…Для оценки ФР в режиме реального времени используется оценка показателя Херста в скользящем окне размера L. Для нейтрализации резких выбросов и уменьшения дисперсии искажений в работе [9]…”

Section: экспериментальная оценка статистических параметров фрunclassified

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IoT Traffic Fractal Dimension Statistical Characteristics on the Kitsune Dataset Example

Shelukhin,

Rybakov

2023

jour

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The paper considers a method for estimating the fractal properties of traffic, and also evaluates the statistical parameters of the fractal dimension of IoT traffic. An analysis of real traffic with attacks from the Kitsune dump and an analysis of the fractal properties of traffic in normal mode and under the influence of attacks such as SSDP Flood, Mirai, OS Scan showed that jumps in the fractal dimension of traffic when attacks occur can be used to create algorithms for detecting computer attacks in IoT networks. Studies have shown that in the case of online analysis of network traffic, when assessing the RF, preference should be given to the modified algorithm for estimating the Hurst exponent in a sliding analysis window.

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Section: постановка задачиunclassified

Section: экспериментальная оценка статистических параметров фрunclassified

IoT Traffic Fractal Dimension Statistical Characteristics on the Kitsune Dataset Example

Shelukhin,

Rybakov

2023

jour

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PSD and Cross-PSD of Responses of Seven Classes of Fractional Vibrations Driven by fGn, fBm, Fractional OU Process, and von Kármán Process

2024

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This paper gives its contributions in four stages. First, we propose the analytical expressions of power spectrum density (PSD) responses and cross-PSD responses to seven classes of fractional vibrators driven by fractional Gaussian noise (fGn). Second, we put forward the analytical expressions of PSD and cross-PSD responses to seven classes of fractional vibrators excited by fractional Brownian motion (fBm). Third, we present the analytical expressions of PSD and cross-PSD responses to seven classes of fractional vibrators driven by the fractional Ornstein–Uhlenbeck (OU) process. Fourth, we bring forward the analytical expressions of PSD and cross-PSD responses to seven classes of fractional vibrators excited by the von Kármán process. We show that the statistical dependences of the responses to seven classes of fractional vibrators follow those of the excitation of fGn, fBm, the OU process, or the von Kármán process. We also demonstrate the obvious effects of fractional orders on the responses to seven classes of fractional vibrations. In addition, we newly introduce class VII fractional vibrators, their frequency transfer function, and their impulse response in this research.

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Modified Algorithm for Detecting Network Attacks Using the Fractal Dimension Jump Estimation Method in Online Mode

Cited by 2 publications

References 14 publications

IoT Traffic Fractal Dimension Statistical Characteristics on the Kitsune Dataset Example

IoT Traffic Fractal Dimension Statistical Characteristics on the Kitsune Dataset Example

PSD and Cross-PSD of Responses of Seven Classes of Fractional Vibrations Driven by fGn, fBm, Fractional OU Process, and von Kármán Process

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