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MF-adaboost: LDoS attack detection based on multi-features and improved adaboost

FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 106卷 347-359页

作者： Tang, Dan Tang, Liu Dai, Rui Chen, Jingwen Li, Xiong Rodrigues, Joel J. P. C. Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Univ Elect Sci & Technol China Sch Comp Sci & Engn Inst Cyber Secur Chengdu 611731 Peoples R China Univ Fed Piaui Teresina PI Brazil Inst Telecomunicacoes Lisbon Portugal

A low-rate denial of service (LDoS) attack is a precise network attack that aims at reducing the quality of the network service. Many networks do not have an effective mechanism for defending against LDoS attacks, including the emerging Internet of Things. In this paper, we propose an LDoS attack detection method that is based on multiple features of network traffic and an improved adaboost algorithm (MF-adaboost). Based on an analysis of the network traffic, we construct a network feature set that is used for feature calculation and feature selection of network traffic data. Feature calculation can extract the most useful information from the network traffic data and reduce the scale of the network data. Feature selection is used to select the optimal classification features to ensure that the detection algorithm can be effectively trained. This method utilizes the adaboost algorithm, which is a classification algorithm in the field of machine learning. The well-trained adaboost algorithm can effectively identify LDoS attack traffic. We improve the adaboost algorithm to alleviate the imbalance of the sample weights. Experiments are conducted on the NS2 simulation platform and a test-bed platform to evaluate the performance of our method. The experimental results demonstrate that our method can detect LDoS attacks effectively. (C) 2020 Elsevier B.V. All rights reserved.

关键词： LDoS Internet of Things Feature set Feature selection adaboost algorithm Machine learning

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A Novel adaboost Framework With Robust Threshold and Structural Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2018年第1期48卷 64-76页

作者： Zhang, Peng-Bo Yang, Zhi-Xin Univ Macau Dept Electromech Engn Fac Sci & Technol Macau 999078 Peoples R China

The adaboost algorithm is a popular ensemble method that combines several weak learners to boost generalization performance. However, conventional adaboost. RT algorithms suffer from the limitation that the threshold value must be manually specified rather than chosen through a self-adaptive mechanism, which cannot guarantee a result in an optimal model for general cases. In this paper, we present a generic adaboost framework with robust threshold mechanism and structural optimization on regression problems. The error statistics of each weak learner on one given problem dataset is utilized to automate the choice of the optimal cut-off threshold value. In addition, a special single-layer neural network is employed to provide a second opportunity to further adjust the structure and strength the adaption capability of the adaboost regression model. Moreover, to consolidate the theoretical foundation of adaboost algorithms, we are the first to conduct a rigorous and comprehensive theoretical analysis on the proposed approach. We prove that the general bound on the empirical error with a fraction of training examples is always within a limited soft margin, which indicates that our novel algorithm can avoid over-fitting. We further analyze the bounds on the generalization error directly under probably approximately correct learning. The extensive experimental verifications on the UCI benchmarks have demonstrated that the performance of the proposed method is superior to other state-of-the-art ensemble and single learning algorithms. Furthermore, a real-world indoor positioning application has also revealed that the proposed method has higher positioning accuracy and faster speed.

关键词： adaboost algorithm bounds of empirical error and generalization error ensemble method indoor positioning system robust threshold special single-layer neural network structural optimization

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Research on robust control of continuous rotary electro-hydraulic servo motor based on adaboost prediction

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MEASUREMENT & CONTROL 2023年第1-2期56卷 156-171页

作者： Wang, Xiaojing Shi, Guangqiang Liu, Meizhen Feng, Yarning Beijing Univ Civil Engn & Architecture Sch Mech Elect & Vehicle Engn Beijing 102616 Peoples R China

A robust control strategy based on adaboost prediction was proposed for the effects of dynamic uncertainty, parameter perturbation and nonlinear factors such as friction and leakage on the robustness and low-speed performance of the electro-hydraulic servo system. Based on the establishment of the Pol-Ind friction model, according to the mathematical model of the continuous rotary electro-hydraulic servo motor, the structural uncertainty model of the system under parameter perturbation and external perturbation and the generalized state equation of the system are established. The robust controller is designed according to the H-infinity control theory. The system feedback mechanism is designed by using the adaboost based on the RBF neural network algorithm. The real-time input and output of the system are used as the training sample set for repeated training, and the multiple weak neural network predictors obtained by training are used to form a strong predictor to calculate the forecast output and error of the electro-hydraulic servo system, and the robust controller was used for real-time control. Simulation and experiment show that: compared with the wavelet neural network control, the robust controller based on adaboost prediction has a good tracking performance to the slope signal with an input signal of 0.001 degrees/s, and has a high response speed to sinusoidal signal. Under the condition of satisfying the requirement of double 10 index, the maximum response frequency reaches 15 Hz. It can effectively improve the low-speed performance and anti-interference ability of the electro-hydraulic servo system, greatly improve the robustness of the system and expand the frequency band of the system.

关键词： Continuous rotary motor low-speed performance robust control adaboost algorithm

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Software-defined network-based dynamic access control mechanism for internet of vehicles using adaboost

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MULTIMEDIA TOOLS AND APPLICATIONS 2022年 1-25页

作者： Karn, Arodh Lal Sengan, Sudhakar Pustokhin, Denis A. Pustokhina, Irina, V Xian Jiaotong Liverpool Univ Sch Math & Phys Dept Financial & Actuarial Math Suzhou Peoples R China PSN Coll Engn & Technol Dept Comp Sci & Engn Tirunelveli India State Univ Management Dept Logist Moscow 109542 Russia Plekhanov Russian Univ Econ Dept Entrepreneurship & Logist Moscow 117997 Russia

Access Control Models (ACM) must secure the communication system of devices in Internet of Vehicles (IoV) under cloud computing architecture. Existing ACM, on the other hand, struggles to determine the right granularity of permissions when dealing with vast numbers of data in the IoV. Furthermore, IoV is vulnerable to attacks, as attackers can readily exploit existing flaws. Due to insufficient or inefficient ACM, some attacks may succeed. As a result, the authentication mechanism must be reinforced as much as possible using cutting-edge ACM. Methods have been applied to Decisions Making System (DMS) about who has access to what in open distributed information systems like big data, the Internet of Things (IoT), and the cloud experience performance issues because of the number and complexity of the rules and regulations governing who has significant exposure to what. The reasonably significant Access Control (AC) time operational costs have a negative impact on the regular functioning of business services as a consequence. This paper presents a framework for an efficient SDN-involved Dynamic Access system based on adaboost (SDNDAAB) model. The challenges related to the ACM is changed by this proposed model into a Binary Classification Problem (BCP) that either permit access permission or deny them. So, apart from providing dynamic support for the AC's efficient execution amidst IoV, the adaboost algorithm also supports the disseminated application of the decision engine via a Software Defined Network (SDN) controller for predicting the AC. The results show that the proposed model supports better permission decision accuracy than the other models.

关键词： Internet of vehicles adaboost algorithm Software defined network Machine learning Internet of things

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On selection and combination of weak learners in adaboost

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PATTERN RECOGNITION LETTERS 2010年第9期31卷 991-1001页

作者： Gao, Changxin Sang, Nong Tang, Qiling Huazhong Univ Sci & Technol IPRAI Wuhan 430074 Peoples R China Wuhan Polytech Univ Dept Math & Phys Wuhan 430023 Peoples R China

Despite of its great success, two key problems are still unresolved for adaboost algorithms: how to select the most discriminative weak learners and how to optimally combine them. In this paper, a new adaboost algorithm is proposed to make improvement in the two aspects. First, we select the most discriminative weak learners by minimizing a novel distance related criterion, i.e., error-degree-weighted training error metric (ETEM) together with generalization capability metric (GCM), rather than training error rate only. Second, after getting the coefficients that are set empirically, we combine the weak learners optimally by tuning the coefficients using kernel-based perceptron. Experiments with synthetic and real scene data sets show our algorithm outperforms conventional adaboost. (C) 2009 Elsevier B.V. All rights reserved.

关键词： adaboost algorithm Distance related criterion Kernel-based perceptron

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Extreme Learning Machine and adaboost-Based Localization Using CSI and RSSI

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IEEE COMMUNICATIONS LETTERS 2021年第6期25卷 1906-1910页

作者： Yan, Jun Ma, Chuanhui Kang, Bin Wu, Xiaohuan Liu, Huaping Nanjing Univ Posts & Telecommun Coll Telecommun & Informat Engn Nanjing 210003 Peoples R China Nanjing Univ Posts & Telecommun Coll Internet Things Nanjing 210003 Peoples R China Oregon State Univ Sch Elect Engn & Comp Sci EECS Corvallis OR 97331 USA

This letter develops an extreme learning machine (ELM) and adaboost technique for indoor localization using channel state information (CSI) and received signal strength indicators (RSSI), aiming to (a) resolve an issue with ELM in which weight parameters are generated randomly that leads to significant performance variations, (b) to maximize localization performance given the same set of measurement data, and (c) reduce the data storage and computational needs of existing schemes. To this end, we first form the fingerprint training dataset that consists of both CSI and RSSI, and fully extract from it the statistical information such as quartile, kurtosis, skewness, entropy, etc., in addition to the commonly used channel mean and std. The random weight issue of ELM is solved by optimizing the adaboost algorithm to combine multiple weak ELM classifiers and generate a stronger classifier for offline training. We provide experimental results to assess the effectiveness of the proposed scheme in terms of localization accuracy and storage/complexity.

关键词： Localization channel state information extreme learning machine adaboost algorithm

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Face Detection in Complex Background Based on Skin Color Features and Improved adaboost algorithms

Face Detection in Complex Background Based on Skin Color Fea...

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The 2010 IEEE International Conference on Progress in Informatics and Computing

作者： Zhengming Li,Lijie Xue and Fei Tan School of Electrical and Information Engineering Jiangsu University Zhenjiang,China

Face detection is a very hot topic in research application of pattern recognition and computer *** is widely applied in artificial intelligence,video surveillance, identity authentication,human-machine interaction and so on. However,skin color detection has high false positive rate in complex background and adaboost algorithm was not satisfactory for detection of multi-pose and multi-face image. So a novel face detection method combined with skin color detection and an improved adaboost algorithm is proposed in this ***,it applies skin model segmentation and morphological operators to detect skin regions in the image. And according to the geometrical characteristics of the face,it screens the candidate face *** by the improved classifiers in a cascade structure based on adaboost,it achieves more accurate promising regions of *** experiment results show that this face detection algorithm improves the detection speed in both the quality of detection, and it can effectively reduce the error detection rate of single test *** method has a good performance on image with complex *** all,this method has a certain theory value and practical value.

关键词： Face detection Skin-color segmentation adaboost algorithm Weight update Introduction

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Data sanitization against label flipping attacks using adaboost-based semi-supervised learning technology

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SOFT COMPUTING 2021年第23期25卷 14573-14581页

作者： Cheng, Ning Zhang, Hongpo Li, Zhanbo Zhengzhou Univ Sch Informat Engn Zhengzhou 450001 Henan Peoples R China Zhengzhou Univ Cooperat Innovat Ctr Internet Healthcare Zhengzhou 450001 Henan Peoples R China

The label flipping attack is a special poisoning attack in the adversarial environment. The research designed a novel label noise processing framework, the core of which is the semi-supervised learning label correction algorithm based on adaboost (AdaSSL). It can effectively improve the label quality of training data and improve the classification performance of the model. Based on five real UCI datasets, this study chose six classic machine learning algorithms (NB, LR, SVM, DT, KNN and MLP) as the base classifiers to classify them. With a noise level of 0 similar to 20%, we evaluated the classification effect of these classifiers on UCI datasets based on the entropy label flipping attack and the AdaSSL defense algorithm. The experimental results show that the AdaSSL algorithm can effectively improve the robustness of the classifier against label flipping attack. Compared with the most advanced semi-supervised defense algorithm in the literature, the algorithm does not need to use additional datasets. At a noise ratio of 10%, the AdaSSL algorithm is significantly better than state-of-the-art label noise defense technology.

关键词： Label noise detection Machine learning adaboost algorithm Semi-supervised

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Brain Image Segmentation Based on the Hybrid of Back Propagation Neural Network and adaboost System

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JOURNAL OF SIGNAL PROCESSING SYSTEMS FOR SIGNAL IMAGE AND VIDEO TECHNOLOGY 2020年第3期92卷 289-298页

作者： Chao, Zhen Kim, Hee-Joung Yonsei Univ Coll Hlth Sci Dept Radiat Convergence Engn 1 Yonseidae Gil Wonju 26493 Gangwon South Korea Yonsei Univ Coll Hlth Sci Dept Radiol Sci 1 Yonseidae Gil Wonju 26493 Gangwon South Korea

The segmentation of brain magnetic resonance (MR) images can provide more detailed anatomical information, which can be of great help for the proper diagnosis of brain diseases. Therefore, the study of medical image segmentation technology is crucial and necessary. Owing to the presence of equipment noise and the complexity of the brain structure, the existing methods have various shortcomings and their performances are not ideal. In this study, we propose a new method based on back propagation (BP) neural networks and the adaboost algorithm. The BP neural network that we created has a 1-7-1 structure. We trained the system using a gravitational search algorithm. (In this algorithm, we use segmented images, which were obtained by state-of-the-art methods, as ideal output data.) Based on this, we established and trained 10 groups of back propagation neural networks (BPNNs) by applying 10 groups of different data. Subsequently, we adopted the adaboost algorithm to obtain the weight of each BPNN. Finally, we updated the BPNNs by training the gravitational search and adaboost algorithms. In this experiment, we used one group of brain magnetic resonance imaging (MRI) datasets. A comparison with four state-of-the-art segmentation methods through subjective observation and objective evaluation indexes reveals that the proposed method achieved better results for brain MR image segmentation.

关键词： Medical image segmentation Back propagation neural network adaboost algorithm Gravitational search algorithm

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adaboost maximum entropy deep inverse reinforcement learning with truncated gradient

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INFORMATION SCIENCES 2022年 602卷 328-350页

作者： Song, Li Li, Dazi Wang, Xiao Xu, Xin Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100029 Peoples R China Natl Univ Def Technol Coll Intelligence Sci & Technol Changsha 410073 Peoples R China

Studying the representational capacity of neural networks to learn nonlinear rewards is necessary in a complex and nonlinear environment. Over recent years, the maximum entropy deep inverse reinforcement learning algorithm (ME-DIRL) has been increasingly applied to the learning of nonlinear rewards. However, under cases of limited and imbalanced expert demonstration data, complex calculations, or overfitting, the learning nonlinear rewards remains a challenging problem. A novel ME-DIRL with adaboost algorithm (AME-DIRL) is our proposed solution. The focus of AME-DIRL is to utilize the adaboost algorithm. This combines multiple ME-DIRL processes to form a strong learner and thus overcome the imbalance of the data set. Furthermore, to deal with the complex calculations in AME-DIRL, a truncated gradient (TG) method is applied for getting the sparse rewards obtained by the strong learner, thus reducing the model complexity. To prevent overfitting, a correction factor is then added to the linear combination of weak learners. AME-DIRL models the relationship between input features and output rewards. Rewards are approximated by means of a convolutional neural network (CNN) with scaled exponential linear units (SELUs). Numerical results indicate that our proposed AME-DIRL shows higher accuracy in learning rewards when compared with several classical inverse reinforcement learning (IRL) algorithms. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Inverse reinforcement learning Maximum entropy Convolutional Neural Network adaboost algorithm Truncated gradient

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