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Unknown DDoS Attack Detection with fuzzy c-means clustering and Spatial Location constraint Prototype Loss

computers, Materials & continua 2024年第2期78卷 2181-2205页

作者： Thanh-Lam Nguyen HaoKao Thanh-Tuan Nguyen Mong-Fong Horng chin-Shiuh Shieh Department of Electronic Engineering National Kaohsiung University of Science and TechnologyKaohsiung807618Taiwan Department of Electronic and Automation Engineering Nha Trang UniversityNha Trang650000Vietnam

Since its inception,the Internet has been rapidly *** the advancement of science and technology and the explosive growth of the population,the demand for the Internet has been on the *** applications in education,healthcare,entertainment,science,and more are being increasingly deployed based on the ***,malicious threats on the internet are on the rise as *** Denial of Service(DDoS)attacks are among the most common and dangerous threats on the internet *** scale and complexity of DDoS attacks are constantly *** Detection Systems(IDS)have been deployed and have demonstrated their effectiveness in defense against those *** addition,the research of Machine Learning(ML)and Deep Learning(DL)in IDS has gained effective results and significant ***,one of the challenges when applying ML and DL techniques in intrusion detection is the identification of unknown *** attacks,which are not encountered during the system’s training,can lead to misclassification with significant *** this research,we focused on addressing the issue of Unknown Attack Detection,combining two methods:Spatial Location constraint Prototype Loss(SLcPL)and fuzzy c-means(FcM).With the proposed method,we achieved promising results compared to traditional *** proposed method demonstrates a very high accuracy of up to 99.8%with a low false positive rate for known attacks on the Intrusion Detection Evaluation Dataset(cIcIDS2017)***,the accuracy is also very high,reaching 99.7%,and the precision goes up to 99.9%for unknown DDoS attacks on the DDoS Evaluation Dataset(cIcDDoS2019)*** success of the proposed method is due to the combination of SLcPL,an advanced Open-Set Recognition(OSR)technique,and FcM,a traditional yet highly applicable clustering *** has yielded a novel method in the field of unknown attack *** further expands the trend of applying DL and M

关键词： cybersecurity DDoS unknown attack detection machine learning deep learning incremental learning convolutional neural networks(cNN) open-set recognition(OSR) spatial location constraint prototype loss fuzzy c-means cIcIDS2017 cIcDDoS2019

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fuzzy c-means Rule Generation for fuzzy Entry Temperature Prediction in a Hot Strip Mill

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Journal of Iron and Steel Research International 2016年第2期23卷 116-123页

作者： Jose Angel BARRIOS cesar VILLANUEVA Alberto cAVAZOS Rafael cOLAS Mechanical and Electrical Engineering Faculty Autonomous University of Nuevo LeonSan Nicolas de los Garza 66451Nuevo LeonMexico Hot Rolling Department Ternium ChurubuscoMonterrey 64000Nuevo LeonMexico

Variable estimation for finishing mill set-up in hot rolling is greatly affected by measurement uncertainties, variations in the incoming bar conditions and product changes. The fuzzy c-means algorithm was evaluated for rule base generation for fuzzy and fuzzy grey-box temperature estimation. Experimental data were collected from a real- life mill and three different sets were randomly drawn. The first set was used for rule-generation, the second set was used for training those systems with learning capabilities, while the third one was used for validation. The perform- ance of the developed systems was evaluated by five performance measures applied over the prediction error with the validation set and was compared with that of the empirical rule-base fuzzy systems and the physical model used in plant. The results show that the fuzzy c-means generated rule-bases improve temperature estimation; however, the best results are obtained when fuzzy c-means algorithm, grey-box modeling and learning functions are combined. Application of fuzzy c-means rule generation brings improvement on performance of up to 72%.

关键词： gray-box modeling ANFIS hot rolling temperature estimation fuzzy c-means rule base generation

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fuzzy c-means clustering based on spatial neighborhood information for image segmentation

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Journal of Systems Engineering and Electronics 2010年第2期21卷 323-328页

作者： Yanling Li Yi Shen Institute of Systems Engineering Department of Control Science and EngineeringHuazhong University of Science and Technology Wuhan 430074 P. R. China college of computer and Information Technology Xinyang Normal University Xinyang 464000 P. R. China

fuzzy c-means （FcM） algorithm is one of the most popular methods for image segmentation. However, the standard FcM algorithm is sensitive to noise because of not taking into account the spatial information in the image. An improved FcM algorithm is proposed to improve the antinoise performance of FcM algorithm. The new algorithm is formulated by incorporating the spatial neighborhood information into the membership function for clustering. The distribution statistics of the neighborhood pixels and the prior probability are used to form a new membership func- tion. It is not only effective to remove the noise spots but also can reduce the misclassified pixels. Experimental results indicate that the proposed algorithm is more accurate and robust to noise than the standard FcM algorithm.

关键词： image segmentation fuzzy c-means spatial informa- tion. robust.

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fuzzy.c-means IN FINDING SUBTYPES OF cANcERS IN cANcER DATABASE

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Journal of Innovative Optical Health Sciences 2014年第1期7卷 109-128页

作者： S.R.KANNAN S.RAMTHILAGAM R.DEVI T.P.HONG Department of Mathematics Pondicherry central University India Department of Mathematics Periyar Government college Tamil Nadu Department of computer Science and Information Engineering National University of Kaohsiung Taiwan

Finding subtypes of cancer in breast cancer database is an extremely dificult task because ofheavy noise by measurement *** of the recent clustering techniques for breast cancerdatabase to achieve cancerous and noncancerous often weigh down the interpretability of ***,this paper tries to find effective fuzzy c-means-based clustering techniques toidentify the proper subtypes of cancer in breast cancer database,This paper obtains the objectivefunction of ffective fuzzy c-means clustering techriques by incorporating the kermel induceddist ance function,Renyi's entropy function,weighted dist ance measure and neighborhood ternsbased spatial *** efectiveness of the proposed methods are proved through the ex-perimental works on Lung cancer database,IRIS dataset,Wine dat aset,checkerboard dataset,Time Series dataset and Yeast ***,the proposed methods are implemented suc-cesfully to cluster the breast cancer dat abase into cancerous and *** clusteringaccuracy has been validat ed through error matrix and silhouette method.

关键词： fuzzy c-means kenel induced distance entropy terms cancer database

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Residual-driven fuzzy c-means clustering for Image Segmentation

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IEEE/cAA Journal of Automatica Sinica 2021年第4期8卷 876-889页

作者： cong Wang Witold Pedrycz ZhiWu Li Mengchu Zhou Department of Electrical and computer Engineering University of AlbertaEdmontonAB T6R 2V4Canada School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071China the Faculty of Engineering King Abdulaziz UniversityJeddah 21589Saudi Arabia School of Electro-Mechanical Engineering Xidian UniversityXi’an 710071and also with the Institute of Systems EngineeringMacao University of Science and TechnologyMacaoChina Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China the Helen and John c.Hartmann Department of Electrical and computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA

In this paper,we elaborate on residual-driven fuzzy c-means(FcM)for image segmentation,which is the first approach that realizes accurate residual(noise/outliers)estimation and enables noise-free image to participate in *** propose a residual-driven FcM framework by integrating into FcM a residual-related regularization term derived from the distribution characteristic of different types of *** on this framework,a weighted?2-norm regularization term is presented by weighting mixed noise distribution,thus resulting in a universal residual-driven FcM algorithm in presence of mixed or unknown ***,with the constraint of spatial information,the residual estimation becomes more reliable than that only considering an observed image *** experiments on synthetic,medical,and real-world images are *** results demonstrate the superior effectiveness and efficiency of the proposed algorithm over its peers.

关键词： fuzzy c-means image segmentation mixed or unknown noise residual-driven weighted regularization

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A Modified fuzzy c-means Algorithm for Brain MR Image Segmentation and Bias Field correction

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Journal of computer Science & Technology 2016年第3期31卷 501-511页

作者： Wen-Qian Deng Xue-Mei Li Xifeng Gao cai-Ming Zhang School of computer Science and Technology Shandong University Jinan 250101 China Department of computer Science University of Houston Houston TX 77004 U.S.A.

In quantitative brain image analysis, accurate brain tissue segmentation from brain magnetic resonance image （MRI） is a critical step. It is considered to be the most important and difficult issue in the field of medical image processing. The quality of MR images is influenced by partial volume effect, noise, and intensity inhomogeneity, which render the segmentation task extremely challenging. We present a novel fuzzy c-means algorithm （RcLFcM） for segmentation and bias field correction of brain MR images. We employ a new gray-difference coefficient and design a new impact factor to measure the effect of neighbor pixels, so that the robustness of anti-noise can be enhanced. Moreover, we redefine the objective function of FcM （fuzzy c-means） by adding the bias field estimation model to overcome the intensity inhomogeneity in the image and segment the brain MR images simultaneously. We also construct a new spatial function by combining pixel gray value dissimilarity with its membership, and make full use of the space information between pixels to update the membership. compared with other state-of-the-art approaches by using similarity accuracy on synthetic MR images with different levels of noise and intensity inhomogeneity, the proposed algorithm generates the results with high accuracy and robustness to ***.

关键词： image segmentation fuzzy c-means bias field correction anti-noise

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Defect inspection of flip chip package using SAM technology and fuzzy c-means algorithm

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Science china(Technological Sciences) 2018年第9期61卷 1426-1430页

作者： LU XiangNing LIU Fan HE ZhenZhi LI LiYi HU NingNing SU Lei School of Mechanical & Electrical Engineering Jiangsu Normal University School of Materials Science and Engineering Georgia Institute of Technology School of Mechanical Engineering Jiangnan University

Solder bumps are widely used in surface mount components, which provide electrical and mechanical connection between the chip/package and the substrate. As the solder bump getting smaller in dimension and pitch, it becomes more difficult to inspect the solder defects hidden in the Ic package. In this paper, an intelligent inspection method using the scanning acoustic microscopy(SAM) and the fuzzy c-means(FcM) algorithm was investigated. A flip chip package of FA10 was chosen as the test sample. The SAM tests of FA10 were carried out in c-scan mode. The sub-image of every solder bump was segmented from the SAM image. The statistical features were then calculated and adopted for clustering of solder bumps using the FcM algorithm. The recognition results of FcM reached a high accuracy of 94.3%. The intelligent system is effective for defect inspection in high density packages.

关键词： solder bump defects inspection SAM technology fuzzy c-means

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Process disturbances monitoring and recognition of short-circuiting GMAW by fuzzy c-means system

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china Welding 2011年第4期20卷 28-33页

作者：胡庆贤王顺尧王艳辉 Provincial Key Lab of Advanced Welding Technology Jiangsu University of Science and Technology Zhenjiang 212002

An intelligent fuzzy c-means system for process monitoring and recognition of process disturbances during short- circuiting gas metal arc welding （GMAW） is introduced in this paper. The raw measured and statistically test data of probability density distribution （ PDD ） and class frequency distribution （ cFD ） of welding electrical parameters are further processed into a 7-dimensional array which is designed to describe various welding conditions, and is employed as input vector of the intelligent fuzzy c-means system. The fuzzy c-means system is used to conduct process monitoring and automatic recognition. The correct recognition rate of 24 test data under 8 kinds of welding condition is 92%.

关键词： process monitoring fuzzy c-means process disturbance short-circuiting gas metal arc welding

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Adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering for brain MRI of AD subject

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Visual Informatics 2021年第4期5卷 67-80页

作者： Sukanta Ghosh Amlan Pratim Hazarika Abhijit chandra Rajani K.Mudi Department of Instrumentation&Electronics Engineering Jadavpur UniversityKolkata700106India

Progression of Alzheimer’s disease(AD)bears close proximity with the tissue loss in the medial temporal lobe(MTL)and enlargement of lateral ventricle(LV).The early stage of AD,mild cognitive impairment(McI),can be traced by diagnosing brain MRI scans with advanced fuzzy c-means clustering algorithm that helps to take an appropriate *** this paper,firstly the sparsity is initiated in clustering method that too rician noise is also incorporated for brain MR scans of AD ***,a novel neighbor pixel constrained fuzzy c-means clustering algorithm is designed where topoloty-based selection of parsimonious neighbor pixels is *** adaptability in choice of neighbor pixel class outliers more justified object edge boundary which outperforms a dynamic cluster *** proposed adaptive neighbor constrained deviation sparse variant fuzzy c-means clustering(AN_DsFcM)can withhold imposed sparsity and withstands rician noise at imposed sparse *** novel algorithm is applied for MRI of AD subjects and normative data is acquired to analyse clustering *** data processing pipeline of theoretically plausible proposition is elaborated in *** experimental results are compared with state-of-the-art fuzzy clustering methods for test MRI *** evaluation and statistical measures are studied to meet both image processing and clinical neurophysiology *** the performance of proposed AN_DsFcM is significantly better than other methods.

关键词： MRI fuzzy c-means Neighbor information modeling Sparse Rician noise AD

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Multimode Process Monitoring Based on fuzzy c-means in Locality Preserving Projection Subspace

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chinese Journal of chemical Engineering 2012年第6期20卷 1174-1179页

作者：解翔侍洪波 Key Laboratory of Advanced control and Optimization for chemical Processes East China University of Science and TechnologyMinistry of Education

For complex industrial processes with multiple operational conditions, it is important to develop effective monitoring algorithms to ensure the safety of production processes. This paper proposes a novel monitoring strategy based on fuzzy c-means. The high dimensional historical data are transferred to a low dimensional subspace spanned by locality preserving projection. Then the scores in the novel subspace are classified into several overlapped clusters, each representing an operational mode. The distance statistics of each cluster are integrated though the membership values into a novel BID (Bayesian inference distance) monitoring index. The efficiency and effectiveness of the proposed method are validated though the Tennessee Eastman benchmark process.

关键词： multimode process monitoring fuzzy c-means locality preserving projection integrated monitoring index Tennessee Eastman process

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