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Intrusion detection system combined enhanced random forest with smote algorithm

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING 2022年第1期2022卷 1-20页

作者： Wu, Tao Fan, Honghui Zhu, Hongjin You, Congzhe Zhou, Hongyan Huang, Xianzhen Jiangsu Univ Technol Sch Mech Engn Changzhou 213001 Peoples R China Jiangsu Univ Technol Sch Comp Engn Changzhou 213001 Peoples R China

Network security is subject to malicious attacks from multiple sources, and intrusion detection systems play a key role in maintaining network security. During the training of intrusion detection models, the detection results generally have relatively large false detection rates due to the shortage of training data caused by data imbalance. To address the existing sample imbalance problem, this paper proposes a network intrusion detection algorithm based on the enhanced random forest and synthetic minority oversampling technique (smote) algorithm. First, the method used a hybrid algorithm combining the K-means clustering algorithm with the smote sampling algorithm to increase the number of minor samples and thus achieved a balanced dataset, by which the sample features of minor samples could be learned more effectively. Second, preliminary prediction results were obtained by using enhanced random forest, and then the similarity matrix of network attacks was used to correct the prediction results of voting processing by analyzing the type of network attacks. In this paper, the performance was tested using the NSL-KDD dataset with a classification accuracy of 99.72% on the training set and 78.47% on the test set. Compared with other related papers, our method has some improvement in the classification accuracy of detection.

关键词： Network intrusion detection Data imbalance smote algorithm Enhanced random forest Similarity NSL-KDD

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Dealing with Imbalanced Dataset: A Re-sampling Method Based on the Improved smote algorithm

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2016年第4期45卷 1160-1172页

作者： Xue, Wei Zhang, Jing Renmin Univ China Sch Stat Ctr Appl Stat 59 Zhongguancun Ave Beijing 100872 Peoples R China

Most classification models have presented an imbalanced learning state when dealing with the imbalanced datasets. This article proposes a novel approach for learning from imbalanced datasets, which based on an improved smote (synthetic Minority Over-sampling technique) algorithm. By organically combining the over-sampling and the under-sampling method, this approach aims to choose neighbors targetedly and synthesize samples with different strategy. Experiments show that most classifiers have achieved an ideal performance on the classification problem of the positive and negative class after dealing imbalanced datasets with our algorithm.

关键词： Classification Imbalanced dataset Re-sampling smote algorithm

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The Application of smote algorithm for Unbalanced Data 2018

The Application of SMOTE Algorithm for Unbalanced Data

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International Conference on Artificial Intelligence and Virtual Reality (AIVR) / International Conference on Big Data and Machine Learning (BDML)

作者： Lv, Dong Ma, ZhiCheng Yang, Shibo Li, Xianbo Ma, Zhixin Jiang, Fan Lanzhou Univ Sch Informat Sci & Engn Lanzhou Gansu Peoples R China Gansu Shining Sci & Technol Co Ltd Lanzhou Gansu Peoples R China

ISBN: (纸本)9781450366410

The current power user data is unbalanced when it is used to analyze the behavior of the leakage user. In other words, the normal user data and the leakage user data have an inconsistent scale. When the automatic identification model of the leakage user is established, the analysis of the information of the leakage user's behavior feature is not clear, which leads to the reduction of the model's efficiency of classification. In this paper, we use Python and deal with the leakage user data based on smote algorithm to increase the basic information of the users and extract more accurate leakage user behavior characteristics.

关键词： Unbalanced data Oversampling strategy Under-sampling strategy smote algorithm Leakage users

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Dealing with Imbalanced Dataset: A Re-sampling Method Based on the Improved smote algorithm

Dealing with Imbalanced Dataset: A Re-sampling Method Based ...

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9th Chinese Data Mining and Applied Statistics Cross-Strait Conference

作者： Xue, Wei Zhang, Jing Renmin Univ China Sch Stat Ctr Appl Stat 59 Zhongguancun Ave Beijing 100872 Peoples R China

关键词： Classification Imbalanced dataset Re-sampling smote algorithm

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Imbalance Data Classification Method Based on Improved smote algorithm and Granular Computing 41

Imbalance Data Classification Method Based on Improved SMOTE...

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第41届中国控制会议

作者： QiLiang Dong Wei Lu School of Control Science and Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Classification of imbalanced data is a problem to be solved in practical applications,and sampling algorithm is a more effective *** smote algorithm is a classical oversampling algorithm that provides a strong guideline for dealing with imbalanced datasets,but there are still some limitations,such as blurring the boundary between positive class samples and negative class *** alleviate the limitations,in this study,a new oversampling method called KB-smote(K-means++Borderline-smote) is *** new algorithm first clusters the minority class,next synthesizes new samples between cluster center and borderline points in minority samples,which solves the problem of blurred class boundary of imbalanced datasets,avoids the interference of noisy samples and reduces the impact on the original ***,this paper uses KB-smote as a data preprocessing algorithm,combined with a hypersphere information granular classifier to classify the imbalanced *** the experimental part,the combined classifier of this paper is compared with other classification algorithms on several imbalanced datasets,and the effectiveness of the algorithm is verified.

关键词： Imbalance Data Classification smote algorithm Granular Computing

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smote algorithm applying imbalanced data in higher education

SMOTE algorithm applying imbalanced data in higher education

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2nd Information Technology and Mechatronics Engineering Conference(ITOEC 2016)

作者： Mengjie Zhang Jing Yang School of computer science and technology Guizhou University

In order to improve student achievement level and management ability of college, we focus on to achive the university student achievement warning system. In the practical application of College Students' performance analysis, we find that there was imbalanced data,and the proportion of the students who are dropped out of school and the students are notdropped is serious *** solve the problem, this paper uses smote algorithm to add the minority class data based the matlab platform to get balanced data, which avoids the over fitting problem that using the traditional method.

关键词： imbalanced data smote algorithm higher education

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The Application of smote algorithm for Unbalanced Data

The Application of SMOTE Algorithm for Unbalanced Data

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作者： Dong Lv ZhiCheng Ma Shibo Yang Xianbo Li Zhixin Ma Fan Jiang School of Information Science and Engineering Lanzhou University

The current power user data is unbalanced when it is used to analyze the behavior of the leakage *** other words,the normal user data and the leakage user data have an inconsistent *** the automatic identification model of the leakage user is established,the analysis of the information of the leakage user's behavior feature is not clear,which leads to the reduction of the model's efficiency of *** this paper,we use Python and deal with the leakage user data based on smote algorithm to increase the basic information of the users and extract more accurate leakage user behavior characteristics.

关键词： Unbalanced data Oversampling strategy Under-sampling strategy smote algorithm Leakage users

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A Novel Synthetic Minority Oversampling Technique for Multiclass Imbalance Problems

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IEEE ACCESS 2025年 13卷 6054-6066页

作者： Wang, Jiao Awang, Norhashidah Univ Sains Malaysia Sch Math Sci George Town 11800 Malaysia Puer Univ Sch Math & Stat Puer 665000 Peoples R China

Multi-class imbalanced datasets present significant challenges in many real-world classification tasks, where certain classes are severely underrepresented. This study addresses the classification problems with multi-class imbalanced datasets, which are inherently more complicated than binary imbalanced problems. To tackle this problem, a novel and effective method called the One-vs-One Center Hybrid Synthetic Minority Over-sampling Technique (OCH-smote) algorithm is proposed, which combines the enhanced Synthetic Minority Oversampling Techniques (smote) with the One-vs-One (OVO) decomposition strategy. The OCH-smote algorithm comprises two key components: the OVO strategy is used to decompose the multi-class imbalanced datasets, and the enhanced CH-smote algorithm is used to generate the balanced training datasets to improve the classification performance. The OCH-smote algorithm is extensively evaluated on 18 real-world multi-class imbalanced datasets, using the CART decision tree as the base classifier. The proposed method is compared with classical and state-of-the-art oversampling methods. On average, the OCH-smote algorithm improves P-macro by 8.19%, MAVA by 9.19%, MG by 30.68%, MFM by 8.78%, and Kappa coefficient by 0.0462 across all datasets compared to the baseline methods. The experimental results demonstrate that the OCH-smote algorithm significantly enhances multi-class imbalanced datasets classification performance.

关键词： Classification algorithms Accuracy Measurement Prediction algorithms Data mining Generative adversarial networks Predictive models Focusing Complexity theory Sampling methods Multi-class imbalanced over-sampling smote algorithm one-vs-one approach

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smote-Boost-based sparse Bayesian model for flood prediction

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EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING 2020年第1期2020卷 1-12页

作者： Wu, Yirui Ding, Yukai Feng, Jun Hohai Univ Coll Comp & Informat Nanjing Peoples R China

With a significant development of big data analysis and cloud-fog-edge computing, human-centered computing (HCC) has been a hot research topic worldwide. Essentially, HCC is a cross-disciplinary research domain, in which the core idea is to build an efficient interaction among persons, cyber space, and real world. Inspired by the improvement of HCC on big data analysis, we intend to involve related core and technologies to help solve one of the most important issues in the real world, i.e., flood prediction. To minimize the negative impacts brought by floods, researchers pay special attention to improve the accuracy of flood forecasting with quantity of technologies including HCC. However, historical flood data is essentially imbalanced. Imbalanced data causes machine learning classifiers to be more biased towards patterns with majority samples, resulting in poor classification of pattern with minority samples. In this paper, we propose a novel Synthetic Minority Over-sampling Technique (smote)-Boost-based sparse Bayesian model to perform flood prediction with both high accuracy and robustness. The proposed model consists of three modules, namely, smote-based data enhancement, AdaBoost training strategy, and sparse Bayes model construction. In smote-based data enhancement, we adopt a smote algorithm to effectively cover diverse data modes and generate more samples for prediction pattern with minority samples, which greatly alleviates the problem of imbalanced data by involving experts' analysis and users' intentions. During AdaBoost training strategy, we propose a specifically designed AdaBoost training strategy for sparse Bayesian model, which not only adaptively and inclemently increases prediction ability of Bayesian model, but also prevents its over-fitting performance. Essentially, the design of AdaBoost strategy helps keep balance between prediction ability and model complexity, which offers different but effective models over diverse rivers and users. Finally

关键词： Human-centered intelligent modeling Intelligent human-centered computing technique Big data analyses in HCC Flood prediction smote algorithm

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Financial Risk Early Warning Model Combining smote and Random Forest for Internet Finance Companies

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JOURNAL OF CASES ON INFORMATION TECHNOLOGY 2024年第1期26卷 1页

作者： Zheng, Zhongqin Shanxi Coll Appl Sci & Technol Taiyuan Peoples R China

At present, there have been many achievements on enterprise financial risk early warning model, but relatively few early warning studies focusing on the Internet industry. Research shows that the model has stable recognition accuracy and good prediction performance. The improved smote algorithm based on PCA can realize the equalization of unbalanced data sets and use random forest as a classifier to classify and predict geological data. Because the noise data in the original data set may cause the change of the data distribution after interpolation, it is proposed to combine the PCA algorithm and the smote algorithm, first perform noise reduction and dimension reduction, and then perform data interpolation to improve the classification performance of imbalanced data sets. my country's Internet financial listed companies conduct experiments on research samples, algorithm can better improve the classification accuracy and provide new ideas for the classification and prediction of unbalanced data.

关键词： Financial Risk Early Warning Random Forest smote algorithm

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