# Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks
## 8 Conclusions  
Estimating the value of the Hurst exponent in the R environment showed that long-term relationships characterize network traffic. In the case of high traffic intensity, persistence was maintained, which emphasizes the general characteristics and nature of traffic generated on the examined nodes. Hurst exponent allowed us to analyze the current course, as well as predict future data behavior trends and what can be valuable in terms of security and detection of attacks or anomalies in the network. Thanks to the correct estimation of the Hurst exponent, creating appropriate patterns, we can prepare in advance for alarming situations and respond to a change in trend. Sudden deviations of the exponent value concerning the results obtained may probably be a sign of problems that for network traffic will indicate unusual network behavior potentially related to cybercriminal attack or network failure. Such network traffic analysis methods can be ideal for protecting critical data and maintaining the continuity of internet services offered.  
Due to the threat of cyberterrorism, researchers are developing special systems to detect anomalies in network traffic. We can detect distributed denial of service (DDOS) attacks, which are extremely dangerous for systems, because these attacks can suspend the operation of certain services or servers. Therefore, it is extremely important in today's world to analyze network traffic processes, allowing us to detect anomalies in the network and model the processes occurring in the network traffic.  
The research allowed for the detailed determination of the Hurst parameter and the determination of the most accurate method, which coincides with analytical results. The nature of the traffic (its load) has an impact on the correctness of the analysis. The analyses showed that these methods could be successfully used for high traffic, but for low traffic, their reliability and use are random. The R package met expectations, allowing for the analysis and calculation of Hurst coefficients using various methods and functions. The selection of selected methods is crucial to determine the Hurst coefficient for specific protocols or general network traffic. Not all methods proved to be correct, but R software fully met the requirements previously set.  
Research has shown that with a small number of packets, these values are unbelievable and cannot be used to build \"real traffic rules\". The Hurst coefficient estimation showed that the discrepancy was over 105% for the low traffic method, which confirmed that low-intensity traffic analysis could not be taken as a reliable measure to predict network anomalies. In the case of high intensity, the difference in results obtained reaches up to 20%, which showed that not all algorithms can be used to assess the characteristics of traffic correctly.  
Methodology, P.D. and M.M.; software: formal analysis and investigation, P.D. and M.M.; conceptualization, M.M.; resources, P.D. and M.M.; writing--original draft preparation, P.D. and M.M.; writing--review and editing, P.D. and M.M.; visualization, P.D. and M.M.; supervision, P.D. and M.M.; project administration, P.D. and M.M. All authors have read and agreed to the published version of the manuscript.  
This project was financed by the Minister of Science and Higher Education of the Republic of Poland within the \"Regional Initiative of Excellence\" program for the years 2019-2022. Project number 027/RID/2018/19: amount granted PLN 11,999,900.  
Not applicable.  
We are thankful to the graduate student [PERSON] of Rzeszow University of Technology for supporting us in the collection of useful information.  
The authors declare no conflict of interest.