With the rapid increase in the number of cyber-attacks, detecting and preventing malicious behavior has become more important than ever before. In this study, we propose a method for detecting and classifying malicious behavior in host process data using machine learning algorithms. One of the challenges in this study is dealing with high-dimensional and imbalanced data. To address this, we first preprocessed the data using Principal Component Analysis (PCA) and Uniform Manifold Approximation and Projection (UMAP) to reduce the dimensions of the data and visualize the distribution. We then used the Adaptive Synthetic (ADASYN) and Synthetic Minority Over-sampling Technique (SMOTE) to handle the imbalanced data. We trained and evaluated the performance of the models using various machine learning algorithms, such as K-Nearest Neighbor, Naive Bayes, Random Forest, Autoencoder, and Memory-Augmented Deep Autoencoder (MemAE). Our results show that the preprocessed datasets using both ADASYN and SMOTE significantly improved the performance of all models, achieving higher precision, recall, and F1-Score values. Notably, the best performance was obtained when using the preprocessed dataset (SMOTE) with the MemAE model, yielding an F1-Score of 1.00. The evaluation was also conducted by measuring the Area Under the Receiver Operating Characteristic Curve (AUROC), which showed that all models performed well with an AUROC of over 90%. Our proposed method provides a promising approach for detecting and classifying malicious behavior in host process data using machine learning algorithms, which can be used in various fields such as anomaly detection and medical diagnosis.

Digital holographic stereograms have met expectations in various fields since their introduction. Traditionally, recording large outdoor physical models has required time-consuming and complex processes involving professional tools and technical expertise. This study, however, aims to streamline the process by utilizing simple equipment, such as an iPhone, basic tools, free phone applications, and free software. Four successful experiments were conducted and evaluated using the digital CHIMERA holographic stereogram-printing technique combined with photogrammetry, Gaussian splatting, light detection and ranging (LiDAR), and image interpolation. This approach records large-scale outdoor content more efficiently and effectively. The selected method allows the development and large-scale dissemination of realistic outdoor content holograms to the public. This study demonstrates the feasibility of creating ultra-realistic outdoor holograms using accessible tools and methods, offering potential applications in various fields such as art, education, and entertainment.