# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis  
[PERSON]  
1 Faculty of Electrical and Computer Engineering, Rzeszow University of Technology, al. security threads or denial of access attacks, as well as other types of destructive actions of cyberattacks. There are many tools and methods for testing traffic generated during packet transmission in computer networks. However, testing long-term traffic relationships using detection methods has an advantage over standard methods, as classical ones can detect known types of attacks that have already been previously identified and denoted. They operate on a specific traffic pattern, which is discovered by fittings. The types and methods of attacks are still evolving, so these methods do not keep up with the rapid changes. Therefore, detecting anomalies in traffic is much more flexible and prospective. That is why we base our research on it. The performance and correctness of the proposed method are verified based on the examination of captured network traffic from a simulated network infrastructure in case of the occurrence and absence of different types of attacks. The presented results of the research with the use of multifractal analysis show significant possibilities to detect both short-term attacks and more intense ones. The research covered different types of attacks, and the analysis was carried out for the entire network traffic as well as from the perspective of different services and protocols. As the main method of our research, we the use self-similarity and long-range dependency (LRD) of the network traffic nature. Self-similarity and scaling phenomena have dominated internet traffic analysis. With the identification of the LRD in network traffic, the research community has undergone a mental shift from Poisson and memory-less processes to LRD and bursty processes. The LRD phenomenon and self-similarity can be analyzed using the Hurst (H) parameter and multifractal spectrum analysis. The analysis of the multifractal dependencies of a given set of data allows for determining the characteristics of the tested data. The multifractal decomposition can be used to analyze the processes taking place in small time scales. A recent analysis of the traffic measurements from different communication networks showed that traffic is long-range dependent, or fractal (self-similar). Our article aims to emphasize the superiority of the Hurst parameter estimator based on the multifractal spectrum [4]. Network traffic can be saved both as an increasing number and as a sequence of gaps between adjacent events. The oldest stream model is the Poisson process. As a result of real network traffic research, internal stream correlation was observed. The model based on the Poisson process did not take this feature of generated traffic into account. In this way, further models were developed that more accurately reflect the real nature of network traffic. However, many of them are characterized by autocorrelation, which disappears very quickly (exponentially). These are called short memory processes. Meanwhile, the development of new network services has changed the characteristics of the traffic pattern and capacity. Currently, network traffic, especially in the Internet of Things (IoT) infrastructure, is characterized by a much stronger correlation over long distances. Most real processes, as shown by scientific research, are multifractal. Multifractal analysis and estimation of the Legendre's spectral characteristics can detect the violation of multifractal properties caused by cyber-attacks. Self-similarity is a phenomenon that retains the statistical properties of the model despite changes in the applied time scale. The most visible feature in the characteristics of network traffic indicating the occurrence of self-similarity is the occurrence of densities and dilutions at intervals between events, and the lack of blurring of this feature despite the use of several different time scales (e.g., seconds, minutes, hours) [4].