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2011 International Conference on Energy and Environment(ICEE 2011)

作者： HAN Feng-qing ZHAO Wen-juan Wen Cheng School of Computer Science and Technology Chongqing Univercity of Technology

SVM has a better classification and prediction performance on balance data classification problems,but has a poor performance for imbalance data classification. We analysed the reason of the poor results which were produced by imbalance data classification based on support vector machine *** main purpose of this paper is to introduce the methods of imbalace data classification problem based on SVM,and proposed a new mathod DISVM to solve the imbalance data classifications. Several methods were compared through experiments,the DISVM has a better effective. Evaluation indexes and future areas of research were discussed.

关键词： SVM imbalance data re-sampling separating hyperplane adjustment

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Classification of aviation incident causes using LGBM with improved cross-validation

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Journal of Systems Engineering and Electronics 2024年第2期35卷 396-405页

作者： NI Xiaomei WANG Huawei CHEN Lingzi LIN Ruiguan School of Civil Aviation Nanjing University of Aeronautics and AstronauticsNanjing 211106China School of Aeronautical Engineering Nanjing Vocational University of Industry TechnologyNanjing 210023China

Aviation accidents are currently one of the leading causes of significant injuries and deaths worldwide. This entices researchers to investigate aircraft safety using data analysis approaches based on an advanced machine learning *** assess aviation safety and identify the causes of incidents, a classification model with light gradient boosting machine (LGBM)based on the aviation safety reporting system (ASRS) has been developed. It is improved by k-fold cross-validation with hybrid sampling model (HSCV), which may boost classification performance and maintain data balance. The results show that employing the LGBM-HSCV model can significantly improve accuracy while alleviating data imbalance. Vertical comparison with other cross-validation (CV) methods and lateral comparison with different fold times comprise the comparative approach. Aside from the comparison, two further CV approaches based on the improved method in this study are discussed:one with a different sampling and folding order, and the other with more CV. According to the assessment indices with different methods, the LGBMHSCV model proposed here is effective at detecting incident causes. The improved model for imbalanced data categorization proposed may serve as a point of reference for similar data processing, and the model’s accurate identification of civil aviation incident causes can assist to improve civil aviation safety.

关键词： aviation safety imbalance data light gradient boosting machine(LGBM) cross-validation(CV)

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IDS-INT:Intrusion detection system using transformer-based transfer learning for imbalanced network traffic

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Digital Communications and Networks 2024年第1期10卷 190-204页

作者： Farhan Ullah Shamsher Ullah Gautam Srivastava Jerry Chun-Wei Lin School of Software Northwestern Polytechnical UniversityXian710072ShaanxiChina Knowledge Unit of Systems and Technology University of Management and TechnologySialkot 51040Pakistan Department of Math and Computer Science Brandon UniversityBrandonMB R7A 6A9Canada Department of Computer Science and Math Lebanese American UniversityBeirut1102Lebanon Western Norway University of Applied Sciences BergenNorway Research Centre for Interneural Computing China Medical UniversityTaichungTaiwanChina

A network intrusion detection system is critical for cyber security against llegitimate *** terms of feature perspectives,network traffic may include a variety of elements such as attack reference,attack type,a subcategory of attack,host information,malicious scripts,*** terms of network perspectives,network traffic may contain an imbalanced number of harmful attacks when compared to normal *** is challenging to identify a specific attack due to complex features and data imbalance *** address these issues,this paper proposes an Intrusion Detection System using transformer-based transfer learning for imbalanced Network Traffic(IDS-INT).IDS-INT uses transformer-based transfer learning to learn feature interactions in both network feature representation and imbalanced ***,detailed information about each type of attack is gathered from network interaction descriptions,which include network nodes,attack type,reference,host information,***,the transformer-based transfer learning approach is developed to learn detailed feature representation using their semantic ***,the Synthetic Minority Oversampling Technique(SMOTE)is implemented to balance abnormal traffic and detect minority ***,the Convolution Neural Network(CNN)model is designed to extract deep features from the balanced network ***,the hybrid approach of the CNN-Long Short-Term Memory(CNN-LSTM)model is developed to detect different types of attacks from the deep *** experiments are conducted to test the proposed approach using three standard datasets,i.e.,UNsWNB15,CIC-IDS2017,and *** explainable AI approach is implemented to interpret the proposed method and develop a trustable model.

关键词： Network intrusion detection Transfer learning Features extraction imbalance data Explainable AI Cybersecurity

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MEM-TET: Improved Triplet Network for Intrusion Detection System

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Computers, Materials & Continua 2023年第7期76卷 471-487页

作者： Weifei Wang Jinguo Li Na Zhao Min Liu College of Computer Science and Technology Shanghai University of Electric PowerShanghai201306China National Pilot School of Software Yunnan UniversityKunming650504China

With the advancement of network communication technology,network traffic shows explosive ***,network attacks occur *** intrusion detection systems are still the primary means of detecting ***,two challenges continue to stymie the development of a viable network intrusion detection system:imbalanced training data and new undiscovered ***,this study proposes a unique deep learning-based intrusion detection *** use two independent in-memory autoencoders trained on regular network traffic and attacks to capture the dynamic relationship between traffic features in the presence of unbalanced training *** the original data is fed into the triplet network by forming a triplet with the data reconstructed from the two encoders to ***,the distance relationship between the triples determines whether the traffic is an *** addition,to improve the accuracy of detecting unknown attacks,this research proposes an improved triplet loss function that is used to pull the distances of the same class closer while pushing the distances belonging to different classes farther in the learned feature *** proposed approach’s effectiveness,stability,and significance are evaluated against advanced models on the Android Adware and General Malware dataset(AAGM17),Knowledge Discovery and data Mining Cup 1999(KDDCUP99),Canadian Institute for Cybersecurity Group’s Intrusion Detection Evaluation dataset(CICIDS2017),UNSW-NB15,Network Security Lab-Knowledge Discovery and data Mining(NSL-KDD)*** achieved results confirmed the superiority of the proposed method for the task of network intrusion detection.

关键词： Intrusion detection memory-augmented autoencoder deep metric learning imbalance data

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Credit Risk Prediction Based on Improved ADASYN Sampling and Optimized LightGBM

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Journal of Social Computing 2024年第3期5卷 232-241页

作者： Mei Song He Ma Yi Zhu Mengdi Zhang the School of Computer Science and Technology Jiangsu Normal UniversityXuzhou 221116China the Management Science and Technology Center Jiangsu Normal UniversityXuzhou 221116China

A credit risk prediction model named KM-ADASYN-TL-FLLightGBM(KADT-FLightGBM)is proposed in this ***,to overcome the limitation of traditional sampling methods in dealing with imbalanced datasets,an improved ADASYN sampling with K-means clustering algorithm is ***,the Tomek Links method is used to filter the generated ***,an utilized an optimized LightGBM algorithm with the Focal Loss is employed to training the model using the datasets obtained by the improved ADASYN ***,the comparative analysis between the ensemble model and other different sampling methodologies is conducted on the Lending Club *** results demonstrate that the proposed model effectively minimizes the misclassification of minority classes in credit risk prediction and can be used as a reference for similar studies.

关键词： imbalance data credit risk prediction Focal Loss adaptive hybrid sampling

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An Ensemble Classification Model Based on imbalanced data for Aviation Safety

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Wuhan University Journal of Natural Sciences 2021年第5期26卷 437-443页

作者： NI Xiaomei WANG Huawei LV Shaolan XIONG Minglan Civil Aviation College Nanjing University of Aeronautics and AstronauticsNanjing 21106JiangsuChina

Nowadays aviation accidents have become one of the major causes of severe injuries and fatalities around the world. This attracts the research community to look into aviation safety by applying data analysis techniques based on an advanced machine learning algorithm. An ensemble classification model based on Aviation Safety Reporting System(ASRS) has been proposed to analyze aviation safety targeting the people injured in the *** ensemble classification model shall contain two modules: the data-driven module consisting of data cleaning, feature selection,and imbalanced data division and reorganization, and the modeldriven module stacked by Random Forest(RF), XGBoost(XGB),and Light Gradient Boosting Machine(LGBM) separately. The results indicate that the ensemble model could solve the data imbalance while vastly improving accuracy. LGBM illustrates higher accuracy and faster run in the analysis of a single model of the ASRS-based imbalanced data, while the ensemble model has the best performance in classification at the same time. The ensemble model proposed for imbalanced data classification can provide a certain reference for similar data processing while improving the safety of civil aviation.

关键词： aviation safety Aviation Safety Reporting System(ASRS) ensemble model imbalance data classification Light Gradient Boosting Machine(LGBM)

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Land Cover Classification Based on PSPNet Using Remote Sensing Image

Land Cover Classification Based on PSPNet Using Remote Sensi...

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第40届中国控制会议

作者： Ge Yu Xi Zhang Peking University

In recent years, land cover classification has been modeled as semantic segmentation of remote sensing images,and significant advances have been achieved. Labeled examples for some categories are difficult to obtain manually by photo interpretation or ground survey, thereby causing category imbalance problems. In some categories, intraclass variability is large,but interclass difference is small, causing hard discrimination. To segment pixels accurately, we proposed an improved land cover classification network based on Pyramid Scene Parsing Network. In our network, an adaptation loss based on focal loss is proposed to increase the focus on indistinguishable pixels for category imbalance. Moreover, the network aggregates multiscale features to obtain fused local and global context information using multiple dilated convolutions with various dilation factors,avoiding information loss causing by large intraclass variability and small interclass difference. Experiments were conducted on real land cover datasets. These experiments confirmed the superior performance of the proposed network compared with the state-of-the-art land cover classification models.

关键词： Land cover classification Segmentation Remote sensing image imbalance data

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