Protecting computing infrastructures from cyber threats is one of the priority research topics in network security. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters. From the study, we generated multiple visualizations associated with various window and step sizes to emphasize the effectiveness of the proposed approach in differentiating normal and attack events by forming distinctive clusters. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. For extracting features, various sliding windows and step sizes are tested. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. Due to the complex nature of network traffic event activities caused by continuously changing computing environments and software applications, identifying the patterns is one of the challenging research topics. Analyzing network traffic activities is imperative in network security to detect attack patterns.
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