# Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent
## 6 Conclusions  
The monitoring of data flows in the interconnected communication of IoT networks as wide-area computer networks is an essential issue in the context of network reliability and information security. The ability to detect anomalies in network traffic can significantly improve the management of network infrastructure and security. This paper presents the possibility of using the Hurst coefficient to determine the level of self-similarity of the traffic, which affects the ability to determine the typical operating states as well as the detection of certain anomalies such as an attack, refusal of access, overload and post-failure state. Additionally, this article presents the results of an analysis of traffic in the communication network using a statistical coefficient of similarity and multifracture spectrum. The presented results of the measurements and research confirmed that the analyzed traffic was self-similar and amounted to 0.5-1. The value of the H parameter increased with the increase in the level of network utilization. Therefore, network efficiency was determined by the self-similar properties of network traffic. Some of the most important physical phenomena can significantly increase the LRD in terms of user behavior, data generation, organization and search, traffic aggregation, network control, network control, etc. The results of analytical considerations and experiments showed that the similarity factor could be successfully applied in the analysis of traffic in computer networks and the communication infrastructure of IoT systems. In order to compare the self-similar and multifractal statistical characteristics of the proposed model to those of the real traffic, some statistical data tests were conducted, such as the autocorrelation function and the multifractal spectrum. The values of Hurst's exponent presented in the paper showed that network traffic is characterized by long-term dependencies. The analyzed cases showed that the appropriate network throughput was maintained, which emphasized the general characteristics and character of the traffic generated in nodes. The measured values of [PERSON]'s exponent allowed for an in-depth analysis of the current state of the network, as well as a forecast of the future trend of data behavior. Such use of this methodology enables a user to prepare for the maintenance or change of the trend in a timely manner. Sudden deviations of the exponent value with the obtained results are likely a sign of problems, which, for network traffic, indicates an unusual behavior of the network, possibly related to a cybercrime attack. The solutions studied may be ideal for analyzing traffic in backbone networks for security and the detection of attacks. The tested methods--multifractal analysis, in particular--are sensitive to any deviation of traffic characteristics due to anomalies. Such traffic analysis methods can be ideal for protecting critical data and maintaining the continuity of internet services, including the IoT communication infrastructure. In the proposed approach, the direct time series extraction from the network packet stream can be done quickly enough and for a sufficiently large set of classes to be able to cope with the expected scenarios for IoT network development. Evidence of the existence of multifractal behavior of traffic flows in computer networks has contributed to deeper traffic understanding and modeling. Through multifractal interpretation, an explanation of traffic behavior in timescales that are smaller than some hundreds of milliseconds is possible. Conceptualization, P.D. and M.M. ; methodology, P.D. and M.M. ; software--formal analysis and investigation, P.D. and 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.