# An Innovative Approach to Anomaly Detection in Communication Networks Using Multifractal Analysis
## 2 Literature Review  
Fractal and multifractal analysis can help to uncover the structure of all kinds of systems in order to have a better understanding of such systems. In particular, both approaches have many different, exciting applications in the security of IT and IoT systems. The massive increase in data sent through various devices and communication protocols has raised serious security concerns that have increased the importance of developing advanced Intrusion Detection Systems (IDS). In [5], the authors propose the use of multifractal analysis and Kalman filter for analyzing the security of Cyber-Physical Systems (CPS). The first stage of the proposed approach is data reduction due to time series processing. The second stage is the detection of anomalies, and the third stage is the prediction of the attack. For the detection of anomalies, it was proposed to use multifractal analysis and time series forecasting. The Kalman filter is used to detect cyber-attacks at an early stage because it accurately predicts the state of the system. The effectiveness of the proposed approach is confirmed by the results of the conducted experiments and analysis presented in the article. The authors in [6] propose the use of multifractal analysis to detect anomalies of backbone network traffic that indicate network failures or attacks. Multifractal spectrum characteristics are used as safety indicators. The effectiveness of the proposed approach is confirmed by experimental results on the detection of Denial of Service (DoS) attacks. Host-Based Intrusion Detection Systems (HIDS) are gaining traction in discovering malicious software inside a host operating system. In [7], the authors have developed a new cognitive host-based anomaly detection system based on a supervised AdaBoost machine learning algorithm. Notably, an information fractal dimension-based approach is incorporated in the original AdaBoost machine learning algorithm to assign a higher weight to the classifier that estimates the wrong hypothesis. The result of the empirical dataset showed that the malware detection performance of the proposed algorithm outperforms the original AdaBoost algorithm in detecting positives, including the reduction in false negatives. Another solution for anomaly detection is shown in [8]. The authors present a new anomaly detector for Sketch and MultiScale (SMS) data traffic, based on combining random projections (sketches) with multi-scale analysis, which has low computational complexity. Sketches allow the automatic and reliable extraction of \"normal\" movement and the detection of anomalies without the need for training data. Multi-scale analysis extracts statistical descriptors using the wavelet leader tools recently developed for multi-fractional analysis, without having to select a time scale first. The proposed detector is illustrated using a sizeable recent set of backbone traffic data from the Measurement and Analysis on the WIDE Internet (MAWI) archive and compared with existing indicators. In [9], anomaly detection used a combination of fuzzy C-means clustering and fuzzy interpolation scheme to analyze network traffic and detect malicious network activity. Once malicious activity is detected, IoT-Keeper automatically enforces network access restrictions against IoT device, generating this activity and prevents it from attacking other devices or services. This method shows that IoT-Keeper has a low resource footprint, and it can detect and mitigate various network attacks, without requiring explicit attack signatures or sophisticated hardware. Other techniques also realize the detection of anomalies. In [10], the authors created a network intrusion detection mechanism using machine learning techniques. The model was constructed using a supervised learning methodology, using historical network data and data collected from the Internet.