The network attacks are increasing both in frequency and intensity with the rapid growth of internet of things (IoT) devices. Recently, denial of service (DoS) and distributed denial of service (DDoS) attacks are reported as the most frequent attacks in IoT networks. The traditional security solutions like firewalls, intrusion detection systems, etc., are unable to detect the complex DoS and DDoS attacks since most of them filter the normal and attack traffic based upon the static predefined rules.

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A qualitative and quantitative extension of the chaotic models used to generate self-similar traffic with long-range dependence (LRD) is presented by means of the formulation of a model that considers the use of piecewise affine onedimensional maps. Based on the disaggregation of the temporal series generated, a valid explanation of the behavior of the values of Hurst exponent is proposed and the feasibility of their control from the parameters of the proposed model is shown.

Instructions: 

fGn series used for simulations in the article "Sobre la Generación de Tráfico Autosimilar con Dependencia de Largo Alcance Empleando Mapas Caóticos Unidimensionales Afines por Tramos (Versión Extendida)", "On the Generation of Self-similar with Long-range Dependent Traffic Using Piecewise Affine Chaotic One-dimensional Maps (Extended Version)". Available at:

https://arxiv.org/abs/2104.04135.

https://easychair.org/publications/preprint/Xwx3.

https://osf.io/dsnke/.

They should be used in MATLAB R2009a.

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The Development of an Internet of Things (IoT) Network Traffic Dataset with Simulated Attack Data.

Abstract— This research focuses on the requirements for and the creation of an intrusion detection system (IDS) dataset for an Internet of Things (IoT) network domain.

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This article explores the required amount of time series points from a high-speed computer network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series addresses resulting from the capture of high-speed network traffic, followed by addressing the minimum amount of point required to obtain in accurate estimates of the Hurst exponent.

Instructions: 

fGn series used for simulations in the article "Preliminaries on the Accurate Estimation of the Hurst Exponent Using Time Series".  Available at:

https://arxiv.org/abs/2103.02091.

https://www.techrxiv.org/articles/preprint/Preliminaries_on_the_Accurate....

https://easychair.org/publications/preprint/RQsp.

https://osf.io/3sk7a/.

They should be used in Selfis01b.

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