UNSW-NB15 Computer Security Dataset: Analysis through Visualization
This work is incremental, highlighting dataset-specific problems for researchers in network security and intrusion detection.
The paper analyzed the UNSW-NB15 computer security dataset using visualization techniques and identified class imbalance and class overlap as major issues that need addressing before using the dataset for machine learning classifier development.
This paper presents a visual analysis of the UNSW-NB25 computer network security or intrusion detection dataset in order to detect any issues inherent to this dataset which may require researchers to address before employing this dataset for data-driven model development such as a machine learning classifier. A number of data preprocessing algorithms are applied on the raw data to address common issues such as elimination of redundant features, conversion of nominal features into numerical format and scaling. PCA, t-SNE and K-means clustering algorithms are employed for developing the graphs and plots for visualization. Consequent analysis through visualization identified and illustrated two major problems as class imbalance and class overlap for this dataset. In conclusion, it is necessary to address these two problems of class imbalance and class overlap prior to employing this dataset for any classifier model development.