The growing complexity of cyber attacks necessitates effective methods for distinguishing malicious from benign network traffic within high-dimensional datasets. Traditional visualization techniques often fail to provide clear separation, hindering threat analysis. This paper introduces a systematic PCA-based approach for visualizing cybersecurity data through systematic 3D projections. Utilizing the DARPA 2009 dataset, we compare projections using 9 and 13 principal components, which capture 99.2% and 100% of the variance, respectively. Our findings, supported by silhouette scores and visual evidence, demonstrate that retaining all 13 components consistently yields superior cluster separability for various attack types, highlighting the importance of even marginal variance contributions. We employ randomized projection vectors, inspired by methods like CHIRP, and rank them to identify optimal visualizations. This systematic exploration provides robust and interpretable insights into complex cyber threats.