Infectious diseases have become one of the leading causes of global morbidity and mortality in human life. Therefore, bacteria identification plays a critical role to protect public health from infectious diseases. Optical spectroscopy has been widely used to identify bacteria by yielding unique signature spectra representing the structure and composition of the bacteria. Since bacteria exhibit very close sensing and analytical patterns, it is challenging to determine their distinctions. In this study, we introduce a novel integrative computational approach for analyzing infrared (IR) spectroscopy spectra to identify bacteria by measuring their similarities employing logarithmic-based wavelet features. A visual analytics tool is also developed to support an interactive visual analysis for characterizing bacteria features. Thirty-two signature IR spectra of Escherichia coli (E. coli) and forty spectra of Pseudomonas aeruginosa (P. aeruginosai) collected by an advanced fiber-optic Fourier transform IR (FO-FTIR) spectroscopy sensor system are used for validating the proposed computational approach. Three machine learning techniques are employed to differentiate the bacteria by measuring their performances. Implementing the presented approach, we identified the overall accuracy of 92.5% in classifying the bacteria with Logistic Regression. We also found that both sensitivity and specificity are significantly high, exceeding 92%.

Analyzing network traffic activities is imperative in network security to detect attack patterns. 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. This study focuses on analyzing the effectiveness of integrating Multi-Resolution Analysis (MRA) and visualization in identifying the attack patterns of network traffic activities. In detail, a Discrete Wavelet Transform (DWT) is utilized to extract features from network traffic data and investigate their capability of identifying attacks. For extracting features, various sliding windows and step sizes are tested. Then, visualizations are generated to help users conduct interactive visual analyses to identify abnormal network traffic events. To determine optimal solutions for generating visualizations, an extensive evaluation with multiple intrusion detection datasets has been performed. In addition, classification analysis with three different classification algorithms is managed to understand the effectiveness of using the MRA with visualization. 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. We also found that utilizing MRA with visualization advances network intrusion detection by generating clearly separated visual clusters.

Network traffic data analysis is important for securing our computing environment and data. However, analyzing network traffic data requires tremendous effort because of the complexity of continuously changing network traffic patterns. To assist the user in better understanding and analyzing the network traffic data, an interactive web-based visualization system is designed using multiple coordinated views, supporting a rich set of user interactions. For advancing the capability of analyzing network traffic data, feature extraction is considered along with uncertainty quantification to help the user make precise analyses. The system allows the user to perform a continuous visual analysis by requesting incrementally new subsets of data with updated visual representation. Case studies have been performed to determine the effectiveness of the system. The results from the case studies support that the system is well designed to understand network traffic data by identifying abnormal network traffic patterns.