LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system
Published 2021 View Full Article
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Title
LIO-IDS: Handling class imbalance using LSTM and improved one-vs-one technique in intrusion detection system
Authors
Keywords
Cybersecurity, Network security, Network-based intrusion detection system (NIDS), Class imbalance problem, Long short-term memory (LSTM), Improved one-vs-one technique (I-OVO)
Journal
Computer Networks
Volume 192, Issue -, Pages 108076
Publisher
Elsevier BV
Online
2021-04-07
DOI
10.1016/j.comnet.2021.108076
References
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Related references
Note: Only part of the references are listed.- Synthetic Minority Oversampling Technique for Optimizing Classification Tasks in Botnet and Intrusion-Detection-System Datasets
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- Building an efficient intrusion detection system based on feature selection and ensemble classifier
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- A Semi-Boosted Nested Model With Sensitivity-Based Weighted Binarization for Multi-Domain Network Intrusion Detection
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- HAST-IDS: Learning Hierarchical Spatial-Temporal Features Using Deep Neural Networks to Improve Intrusion Detection
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- Dimensionality reduction with IG-PCA and ensemble classifier for network intrusion detection
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- Learning from class-imbalanced data: Review of methods and applications
- (2017) Guo Haixiang et al. EXPERT SYSTEMS WITH APPLICATIONS
- A method for multi-class sentiment classification based on an improved one-vs-one (OVO) strategy and the support vector machine (SVM) algorithm
- (2017) Yang Liu et al. INFORMATION SCIENCES
- A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
- (2016) Anna L. Buczak et al. IEEE Communications Surveys and Tutorials
- Taxonomy and Survey of Collaborative Intrusion Detection
- (2015) Emmanouil Vasilomanolakis et al. ACM COMPUTING SURVEYS
- DRCW-OVO: Distance-based relative competence weighting combination for One-vs-One strategy in multi-class problems
- (2015) Mikel Galar et al. PATTERN RECOGNITION
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- (2014) Robert Mitchell et al. ACM COMPUTING SURVEYS
- Multi-class AdaBoost
- (2013) Trevor Hastie et al. Statistics and Its Interface
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