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An Efficient Modelling of oversampling with Optimal Deep Learning Enabled Anomaly Detection in Streaming Data

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China Communications 2024年第5期21卷 249-260页

作者： R.Rajakumar S.Sathiya Devi Srinivasa Ramanujan Centre SASTRA Deemed UniversityKumbakonamTamil Nadu 612001India University College of Engineering BIT CampusAnna UniversityTiruchirappalli 620024India

Recently,anomaly detection(AD)in streaming data gained significant attention among research communities due to its applicability in finance,business,healthcare,education,etc.The recent developments of deep learning(DL)models find helpful in the detection and classification of anomalies.This article designs an oversampling with an optimal deep learning-based streaming data classification(OS-ODLSDC)model.The aim of the OSODLSDC model is to recognize and classify the presence of anomalies in the streaming data.The proposed OS-ODLSDC model initially undergoes preprocessing step.Since streaming data is unbalanced,support vector machine(SVM)-Synthetic Minority Over-sampling Technique(SVM-SMOTE)is applied for oversampling process.Besides,the OS-ODLSDC model employs bidirectional long short-term memory(Bi LSTM)for AD and classification.Finally,the root means square propagation(RMSProp)optimizer is applied for optimal hyperparameter tuning of the Bi LSTM model.For ensuring the promising performance of the OS-ODLSDC model,a wide-ranging experimental analysis is performed using three benchmark datasets such as CICIDS 2018,KDD-Cup 1999,and NSL-KDD datasets.

关键词： anomaly detection deep learning hyperparameter optimization oversampling SMOTE streaming data

Learning Vector Quantization-Based Fuzzy Rules oversampling Method

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Computers, Materials & Continua 2024年第6期79卷 5067-5082页

作者： Jiqiang Chen Ranran Han Dongqing Zhang Litao Ma School of Mathematics and Physics Hebei University of EngineeringHandan056038China

Imbalanced datasets are common in practical applications,and oversampling methods using fuzzy rules have been shown to enhance the classification performance of imbalanced data by taking into account the relationship between data attributes.However,the creation of fuzzy rules typically depends on expert knowledge,which may not fully leverage the label information in training data and may be subjective.To address this issue,a novel fuzzy rule oversampling approach is developed based on the learning vector quantization(LVQ)algorithm.In this method,the label information of the training data is utilized to determine the antecedent part of If-Then fuzzy rules by dynamically dividing attribute intervals using LVQ.Subsequently,fuzzy rules are generated and adjusted to calculate rule weights.The number of new samples to be synthesized for each rule is then computed,and samples from the minority class are synthesized based on the newly generated fuzzy rules.This results in the establishment of a fuzzy rule oversampling method based on LVQ.To evaluate the effectiveness of this method,comparative experiments are conducted on 12 publicly available imbalance datasets with five other sampling techniques in combination with the support function machine.The experimental results demonstrate that the proposed method can significantly enhance the classification algorithm across seven performance indicators,including a boost of 2.15%to 12.34%in Accuracy,6.11%to 27.06%in G-mean,and 4.69%to 18.78%in AUC.These show that the proposed method is capable of more efficiently improving the classification performance of imbalanced data.

关键词： oversampling fuzzy rules learning vector quantization imbalanced data support function machine

Joint Sample Position Based Noise Filtering and Mean Shift Clustering for Imbalanced Classification Learning

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Tsinghua Science and Technology 2024年第1期29卷 216-231页

作者： Lilong Duan Wei Xue Jun Huang Xiao Zheng School of Computer Science and Technology Anhui University of TechnologyMaanshan 243032China Institute of Artificial Intelligence Hefei Comprehensive National Science CenterHefei 230088China

The problem of imbalanced data classification learning has received much attention.Conventional classification algorithms are susceptible to data skew to favor majority samples and ignore minority samples.Majority weighted minority oversampling technique(MWMOTE)is an effective approach to solve this problem,however,it may suffer from the shortcomings of inadequate noise filtering and synthesizing the same samples as the original minority data.To this end,we propose an improved MWMOTE method named joint sample position based noise filtering and mean shift clustering(SPMSC)to solve these problems.Firstly,in order to effectively eliminate the effect of noisy samples,SPMsC uses a new noise filtering mechanism to determine whether a minority sample is noisy or not based on its position and distribution relative to the majority sample.Note that MWMOTE may generate duplicate samples,we then employ the mean shift algorithm to cluster minority samples to reduce synthetic replicate samples.Finally,data cleaning is performed on the processed data to further eliminate class overlap.Experiments on extensive benchmark datasets demonstrate the effectiveness of SPMsC compared with other sampling methods.

关键词： imbalanced data classification oversampling noise filtering clustering

Detecting Ethereum Ponzi Schemes Through Opcode Context Analysis and oversampling-Based AdaBoost Algorithm

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Computer Systems Science & Engineering 2023年第10期47卷 1023-1042页

作者： Mengxiao Wang Jing Huang Faculty of Information Technology Beijing University of TechnologyBeijing100124China Beijing Key Laboratory of Computational Intelligence and Intelligence System Beijing100124China

Due to the anonymity of blockchain,frequent security incidents and attacks occur through it,among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses.Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes.However,there are still some flaws in current research,e.g.,insufficient feature extraction of Ponzi scheme smart contracts,without considering class imbalance.In addition,there is room for improvement in detection precision.Aiming at the above problems,this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting(AdaBoost)algorithm.Firstly,this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features,which helps to improve the feature extraction effect.Meanwhile,adaptive synthetic sampling(ADASYN)is introduced to deal with class imbalanced data,and integrated with the Adaboost classifier.Finally,this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts.Experimentally,this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision.Moreover,this article compares and discusses the state of art methods with our method in four aspects:data acquisition,data preprocessing,feature extraction,and classifier design.Both experiment and discussion validate the effectiveness of our model.

关键词： Blockchain smart Ponzi scheme n-gram oversampling ensemble learning

oversampling Method Based on Gaussian Distribution and K-Means Clustering

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Computers, Materials & Continua 2021年第10期69卷 451-469页

作者： Masoud Muhammed Hassan Adel Sabry Eesa Ahmed Jameel Mohammed Wahab Kh.Arabo Department of Computer Science University of ZakhoDuhok42001Kurdistan RegionIraq Department of Information Technology Duhok Polytechnic UniversityDuhok42001Kurdistan RegionIraq

Learning from imbalanced data is one of the greatest challenging problems in binary classification,and this problem has gained more importance in recent years.When the class distribution is imbalanced,classical machine learning algorithms tend to move strongly towards the majority class and disregard the minority.Therefore,the accuracy may be high,but the model cannot recognize data instances in the minority class to classify them,leading to many misclassifications.Different methods have been proposed in the literature to handle the imbalance problem,but most are complicated and tend to simulate unnecessary noise.In this paper,we propose a simple oversampling method based on Multivariate Gaussian distribution and K-means clustering,called GK-Means.The new method aims to avoid generating noise and control imbalances between and within classes.Various experiments have been carried out with six classifiers and four oversampling methods.Experimental results on different imbalanced datasets show that the proposed GK-Means outperforms other oversampling methods and improves classification performance as measured by F1-score and Accuracy.

关键词： Class imbalance oversampling gaussian multivariate distribution k-means clustering

oversampling analysis in fractional Fourier domain

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Science in China(Series F) 2009年第8期52卷 1446-1455页

作者： ZHANG Feng TAO Ran WANG Yue Department of Electronic Engineering Beijing Institute of Technology Beijing 100081 China

oversampling is widely used in practical applications of digital signal processing. As the fractional Fourier transform has been developed and applied in signal processing fields, it is necessary to consider the oversampling theorem in the fractional Fourier domain. In this paper, the oversampling theorem in the fractional Fourier domain is analyzed. The fractional Fourier spectral relation between the original oversampled sequence and its subsequences is derived first, and then the expression for exact reconstruction of the missing samples in terms of the subsequences is obtained. Moreover, by taking a chirp signal as an example, it is shown that, reconstruction of the missing samples in the oversampled signal is suitable in the fractional Fourier domain for the signal whose time-frequency distribution has the minimum support in the fractional Fourier domain.

关键词： fractional Fourier transform oversampling chirp signal

Boosting imbalanced data learning with Wiener process oversampling

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Frontiers of Computer Science 2017年第5期11卷 836-851页

作者： Qian LI Gang LI Wenjia NIU Yanan CAO Liang CHANG Jianlong TAN Li GUO Institute of Information Engineering Chinese Academy of Sciences Beijing 100093 China School of Information Technology Deakin University Geelong VIC 3125 Australia Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Guilin 541004 China

Learning from imbalanced data is a challenging task in a wide range of applications, which attracts significant research efforts from machine learning and data mining community. As a natural approach to this issue, oversampling balances the training samples through replicating existing samples or synthesizing new samples. In general, synthesization outperforms replication by supplying additional information on the minority class. However, the additional information needs to follow the same normal distribution of the training set, which further constrains the new samples within the predefined range of training set. In this paper, we present the Wiener process oversampling （WPO） technique that brings the physics phenomena into sample synthesization. WPO constructs a robust decision region by expanding the attribute ranges in training set while keeping the same normal distribution. The satisfactory performance of WPO can be achieved with much lower computing complexity. In addition, by integrating WPO with ensemble learning, the WPOBoost algorithm outperforms many prevalent imbalance learning solutions.

关键词： imbalanced-data learning oversampling ensemble learning Wiener process AdaBoost

Using deep learning to detect small targets in infrared oversampling images

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Journal of Systems Engineering and Electronics 2018年第5期29卷 947-952页

作者： LIN Liangkui WANG Shaoyou TANG Zhongxing Shanghai Institute of Satellite Engineering Shanghai 201109China

According to the oversampling imaging characteristics, an infrared small target detection method based on deep learning is proposed. A 7-layer deep convolutional neural network(CNN) is designed to automatically extract small target features and suppress clutters in an end-to-end manner. The input of CNN is an original oversampling image while the output is a cluttersuppressed feature map. The CNN contains only convolution and non-linear operations, and the resolution of the output feature map is the same as that of the input image. The L1-norm loss function is used, and a mass of training data is generated to train the network effectively. Results show that compared with several baseline methods, the proposed method improves the signal clutter ratio gain and background suppression factor by 3–4 orders of magnitude, and has more powerful target detection performance.

关键词： infrared small target detection oversampling deep learning convolutional neural network(CNN)

Markovian Cascaded Channel Estimation for RIS Aided Massive MIMO Using 1⁃Bit ADCs and oversampling

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ZTE Communications 2022年第1期20卷 48-56页

作者： SHAO Zhichao YAN Wenjing YUAN Xiaojun National Key Laboratory of Science and Technology on Communica-tions University of Electronic Science and Technology of ChinaChengdu 611731China

A reconfigurable intelligent surface(RIS)aided massive multiple-input multiple-output(MIMO)system is considered,where the base station employs a large antenna array with low-cost and low-power 1-bit analog-to-digital converters(ADCs).To compensate for the per-formance loss caused by the coarse quantization,oversampling is applied at the receiver.The main challenge for the acquisition of cascaded channel state information in such a system is to handle the distortion caused by the 1-bit quantization and the sample correlation caused by oversampling.In this work,Bussgang decomposition is applied to deal with the coarse quantization,and a Markov chain is developed to char-acterize the banded structure of the oversampling filter.An approximate message-passing based algorithm is proposed for the estimation of the cascaded channels.Simulation results demonstrate that our proposed 1-bit systems with oversampling can approach the 2-bit systems in terms of the mean square error performance while the former consumes much less power at the receiver.

关键词： massive MIMO reconfigurable intelligent surface channel estimation 1-bit ADCs oversampling