Machine-Learning Approach to Optimize SMOTE Ratio in Class Imbalance Dataset for Intrusion Detection

• Published in: Computational intelligence and neuroscience Vol. 2018; no. 2018; pp. 1 - 11
• Main Authors: Seo, Jae-Hyun, Kim, Yong-Hyuk
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01.01.2018; Hindawi; Hindawi Limited
• Subjects: Algorithms; Artificial intelligence; Classification; Classification - methods; Clustering; Computer Simulation; Cybersecurity; Data Mining; Data processing; Datasets; Decision trees; Humans; International conferences; Intrusion detection systems; Knowledge discovery; Learning algorithms; Machine Learning; Mathematical models; Methods; Neural networks; Optimization; Oversampling; Ratios; ROC Curve; Support vector machines
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2018/9704672

The KDD CUP 1999 intrusion detection dataset was introduced at the third international knowledge discovery and data mining tools competition, and it has been widely used for many studies. The attack types of KDD CUP 1999 dataset are divided into four categories: user to root (U2R), remote to local (R2L), denial of service (DoS), and Probe. We use five classes by adding the normal class. We define the U2R, R2L, and Probe classes, which are each less than 1% of the total dataset, as rare classes. In this study, we attempt to mitigate the class imbalance of the dataset. Using the synthetic minority oversampling technique (SMOTE), we attempted to optimize the SMOTE ratios for the rare classes (U2R, R2L, and Probe). After randomly generating a number of tuples of SMOTE ratios, these tuples were used to create a numerical model for optimizing the SMOTE ratios of the rare classes. The support vector regression was used to create the model. We assigned each instance in the test dataset to the model and chose the best SMOTE ratios. The experiments using machine-learning techniques were conducted using the best ratios. The results using the proposed method were significantly better than those of previous approach and other related work.