# Anomaly Detection in IoT Communication Network Based on Spectral Analysis and Hurst Exponent
## 2 Literature Review
The analysis of real data is a challenge for both engineers and researchers. Therefore, LRD has been increasingly used in data analysis, including traffic. In order to increase efficiency, the authors of [8] used Multifractal Detrended Fluctuation Analysis (MDFA) for the multifractal analysis of discharge signals. Spectral studies of q-order Hurst, mass exponents and multifractals revealed differences between different voltage values applied to different types of signals (electrical and acoustic). The simulated HFD (Higuchi Fractal Dimension) and MDFA algorithms have proven to be useful in the real-time detection and analysis of pressure plate failures where early warning can prevent insulation system malfunctions. Additionally, the simultaneous analysis of electrical and acoustic signals using a synergy of these methods enhances the efficiency of the proposed system. The authors in [7] showed that the multifractal analysis of financial data for one and multidimensional time series arouses great interest in the community of ecophysics, mainly due to the invention of new methods and easier access to a massive amount of financial data. Many new methods of multifractal analysis are still being proposed, most of which are variants of existing classical methods. This paper presents some examples of multifractal analysis used in quantifying market inefficiencies, supporting risk management, and others. The critical task in the empirical multifractal analysis is to determine the appropriate scaling range based on which scaling exponents are estimated. Usually, a small change in the scaling range results in significant changes in the estimated exponents. Very short time series can give \"wrong\" estimates because the estimated multifractal properties usually deviate more from shorter time series, as confirmed by the log-Poisson binomial. In addition, the estimated generalized Hurst exponents may significantly differ from the expected values, especially in the case of short time series. Therefore, statistical tests are necessary for empirical multifractal analysis. It is often the case that scaling exponents and multifractal spectrum derived from empirical time series are problematic, especially when there are linear or non-linear trends in the time series. The result is an erroneous estimation of the degree and nature of the multifractal, which has harmful effects on the understanding of network traffic behavior. The choice of scaling range is most important and challenging for short-term series [7; 14; 15]. The authors in [1] stipulated that models in networks with a large number of sensors and RFID should be well researched. The authors also analyzed USN motion models, and the test results showed that the traffic flows for fixed and mixed fixed/moving sensor nodes are self-similar with an average level of similarity in both cases. The motion flow for reconfiguration and signaling was found to be self-similar with a high level of self-similarity. The area of anomaly detection in networks has attracted much attention in recent years, especially with the development of interconnected devices and social networks. The detection of anomalies covers a wide range of applications, from the detection of terrorist cells in anti-terrorist operations to the identification of unexpected mutations during the transcription of ribonucleic acid. Accordingly, many algorithmic anomaly detection techniques have been implemented. In the paper of [16], a statistical evaluation of a set of popular spectral methods for the detection of anomalies in networks was carried out. The presented studies revealed several essential and critical shortcomings. The authors evaluated the performance of these algorithms by using simulated networks and extended the methods from binary to count networks. Applications such as traffic classification and police control require a scalable approach in real-time. Anomaly detection and security mechanisms require the rapid identification and response of unforeseen events in the processing of millions of different events.