# Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks
## 4 Research Methodology and R Environment  
For the analysis, we selected a home-based network, combining home resources to show the different traffic specifics--both IP and typical IoT traffic with different loads. IoT is a concept that is beginning to appear in our everyday life. Many new household appliances are able to communicate via the Internet by sending data to special applications on our smartphones. Intelligent homes are being created, whose main task is to integrate various household appliances, lighting, windows, etc. Smart homes give us the ability to control these tools. For the sake of simplicity, we analyzed the IoT network based on TCP/IP services. The traffic (load) characteristics of such a network vary over time. It depends not only on the number of users working at a given time but also on the type of tasks performed. In the era of the COVID-19 pandemic, due to remote work and learning, higher load, i.e., the intensity of generated traffic, will be observed during the daytime, working hours, or in the evening, e.g., the use of VoIP, TV on a row, etc. This article presents two extreme situations--two traffic intensities: high (high-intensity traffic) and low (for low traffic). This allowed us to confirm that the traffic is self-like and can be analyzed only with normal high traffic intensity work, and potential anomalies can be detected. For low intensity, as shown by the broadcast, the values obtained for all methods are around 0.5, which indicates that traffic is antipersistent time series, i.e., there are negative correlations between the series's successive terms. This type of series carries the most significant probability of change, and the risk posed by such a series is the greatest. Thus, there is no possibility of real evaluation. Our model is based on collecting statistics and elaborating traffic \"signatures\" at a given time and the analysis of deviations from the \"normal\" work ratio. Any deviations can and should generate an alert, e.g., to the unauthorized access to our network. This problem is described in detail in the publications [1; 20; 25; 27; 20]. In the case of Hurst values above 0.5, the methods examined proved to be successful, but the discrepancy of results for each of them was so great that we have shown that not all of them can be used to compare to each other. The determination of the Hurst coefficient for each method was correct, but it did not allow for mixing, as these values were entirely different levels. They still showed the proper character of the movement, and their use was, of course, proper. However, the research results showed significant differences between the methods; therefore, we refer to the value with an empirical exponent showing the most stable characteristic of the traffic in the research. The research included an analysis of the processes of network traffic intensity in the LAN and IoT networks. The traffic was generated in a network consisting of three computers and a router. The router guaranteed access to the Internet for each computer or IoT device. The topology of the network is shown in Figure 1. One of the computers was equipped with Wireshark program for collecting packets in the local network and the Internet. The packets were collected within 1 hour for two cases: high network traffic (a web browser running with several web pages, applications using the Internet in the background, online computer games generating network traffic) and network traffic containing the low intensity of network packets. R is a flexible (free) analytical environment with rich functionality that is used in much research and practical work on data analysis and knowledge discovery. Moreover, it provides embedded operations to facilitate the processing of tabular datasets; graphical data description mechanisms; rich libraries of analytical functions, including a wide range of statistical methods and knowledge discovery methods; and, most importantly, an interactive command interpreter and (for some platforms) a graphical user interface [29; 30].