# Influence of Model and Traffic Pattern on Determining the Self-Similarity in IP Networks
## 2 Literature Review
To prevent inaccurate traffic statistics, [PERSON] et al. applied several static sampling strategies to network traffic characteristics. As shown in [19], static sampling can cause inaccurate traffic statistics. This paper develops and evaluates adaptive sampling methods to eliminate static sampling inaccuracies. This allowed for the estimation of the Hurst parameter for static and adaptive tests. It was shown that adaptive sampling results in a more accurate estimation of the mean, variance, and Hurst parameter. Using IP addresses to determine the identity of interconnected units and traffic made it perfectly obvious. That is why one could consider that a comparable paralleling could be used in IoT for reconciliation purposes, but this is not so apparent. Many other technologies and wireless communication networks have been evolved by the industry sector to satisfy the demands of IoT applications, which has led to many issues with respect to interoperability. A number of suppliers began without IPv6 protocol integration but have been gradually incorporating it, which would enable the use of our traffic analysis methodology. The Internet of things traffic includes a series of packets with timestamps that represent a time series. The data of the time series are a collection of values derived for the sequential time measurements [20]. There are many examples of emerging types of network activity that have developed from fundamental internet protocols such as transmission control protocol (TCP) and certain types of services such as TELNET and FTP (file transfer protocol). These applications, such as video streaming, have brought with them new traffic patterns that are essential for traffic engineering. If connected to a network, IoT units generate traffic (inbound and outbound) in accordance with certain setup options and software services. Although various network equipment can operate with multiple protocols and transfer data for different destinations, the large majority of such traffic operates with TCP/IP protocols. They cover network setup traffic (e.g., network time protocol (NTP) and domain name system (DNS)) and regular transmission between the host and the server. For this reason, our research was focused on TCP/IP traffic analysis. Because of the broad implementation of security protocols such as secure soscl, transport layer security (TLS), and privacy policy, it is possible to classify only the packet header for traffic. On the basis of traffic volume, packet length, network protocols, and traffic direction, i.e., inbound and outbound, we are able to isolate and monitor users' packets. User packets contain user data and server-host communication packets (TCP, user datagram protocol (UDP), hypertext transfer protocol (HTTP), or other multi-layer protocols). The control packets primarily handle packets of such functional protocols as ICMP (internet control message protocol), ARP (address resolution protocol), DNS, and NTP packets. The self-similarity-based network traffic anomaly detection scheme consisted of several modules: traffic collection, statistical analysis, statistical estimation, and anomaly detection. The router's load is mirrored on the traffic acquisition server in order to minimize the effect on standard network operation while gathering traffic in the local area network (LAN). This traffic received from the router is handled. Certain traffic characteristics such as the number of packets and their total length may be distinguished. The aim of the research was to analyze network traffic and identify if there are long-term dependencies during network operation and overtime intervals. To carry out work with every caught packet, we separated those that had the greatest influence on the network. It was classified into main categories in terms of services and protocols: HTTP, HTTPS (HTTP secure), unknown, IP security (IPSec), DNS, secure shel (SSH), and others [1,2]. Very interesting applications of the Hurst coefficient can be found in the publication of [21].