Borderline SMOTE Algorithm and Feature Selection‐Based Network Anomalies Detection Strategy

• Main Authors: Cai, Q. (Author), Kong, Z. (Author), Li, J. (Author), Que, H. (Author), Sun, Y. (Author), Wang, S. (Author), Zhao, J. (Author)
• Format: Article
• Language: English
• Published: MDPI 2022
• Subjects: Algorithm selection; Anomaly detection; borderline SMOTE; Borderline SMOTE; Decision trees; Feature Selection; Features selection; information gain ratio; Information gain ratio; Intrusion detection; Learning algorithms; machine learning; Machine-learning; Network anomaly detection; network intrusion detection; Network intrusion detection; Network intrusions; Selection based; SMOTE algorithm
• Online Access: View Fulltext in Publisher
• ISBN: 19961073 (ISSN)
• DOI: 10.3390/en15134751

This paper proposes a novel network anomaly detection framework based on data balance and feature selection. Different from the previous binary classification of network intrusion, the network anomaly detection strategy proposed in this paper solves the problem of multiple classification of network intrusion. Regarding the common data imbalance of a network intrusion detection set, a resampling strategy generated by random sampling and Borderline SMOTE data is developed for data balance. According to the features of the intrusion detection dataset, feature selection is carried out based on information gain rate. Experiments are carried out on three basic machine learning algorithms (K‐nearest neighbor algorithm (KNN), decision tree (DT), random forest (RF)), and the optimal feature selection scheme is obtained. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.