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Research on Intrusion Detection Algorithm Based on multi-class SVM in Wireless Sensor Networks

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Communications and Network 2013年第3期5卷 524-528页

作者： Hangxia Zhou Qian Liu Chen Cui Department of Computer Science and Technology China Jiliang University Hangzhou China

A multi-class method is proposed based on Error Correcting Output Codes algorithm in order to get better performance of attack recognition in Wireless Sensor Networks. Aiming to enhance the accuracy of attack detection, the multi-class method is constructed with Hadamard matrix and two-class Support Vector Machines. In order to minimize the complexity of the algorithm, sparse coding method is applied in this paper. The comprehensive experimental results show that this modified multi-class method has better attack detection rate compared with other three coding algorithms, and its time efficiency is higher than Hadamard coding algorithm.

关键词： Wireless Sensor Network multi-class Network Security

Power Quality Disturbance classification Method Based on Wavelet Transform and SVM multi-class Algorithms

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Energy and Power Engineering 2013年第4期5卷 561-565页

作者： Xiao Fei Southwest Jiaotong University School of Electrical Engineering ChengDu China

The accurate identification and classification of various power quality disturbances are keys to ensuring high-quality electrical energy. In this study, the statistical characteristics of the disturbance signal of wavelet transform coefficients and wavelet transform energy distribution constitute feature vectors. These vectors are then trained and tested using SVM multi-class algorithms. Experimental results demonstrate that the SVM multi-class algorithms, which use the Gaussian radial basis function, exponential radial basis function, and hyperbolic tangent function as basis functions, are suitable methods for power quality disturbance classification.

关键词： Power Quality Disturbance classification Wavelet Transform SVM multi-class Algorithms

Scheduler Algorithm for multi-class Switch with Priority Threshold

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International Journal of Communications, Network and System Sciences 2012年第6期5卷 313-320页

作者： Abdul Aziz Abdul Rahman Kamaruzzaman Seman Kamarudin Saadan Ahmad Kamsani Samingan Azreen Azman Faculty of Computer Science & IT Universiti Putra Malaysia (UPM) Selangor Malaysia Faculty of Science & Technology Universiti Sains Islam Malaysia (USIM) Negeri Sembilan Malaysia Next Generation Access Telekom Research & Development Selangor Malaysia

The requirement for guaranteed Quality of Service (QoS) have become very essential since there are numerous network base application is available such as video conferencing, data streaming, data transfer and many more. This has led to the multi-class switch architecture to cater for the needs for different QoS requirements. The introduction of threshold in multi-class switch to solve the starvation problems in loss sensitive class has increased the mean delay for delay sensitive class. In this research, a new scheduling architecture is introduced to improve mean delay in delay sensitive class when the threshold is active. The proposed architecture has been simulated under uniform and non-uniform traffic to show performance of the switch in terms of mean delay. The results show that the proposed architecture has achieved better performance as compared to Weighted Fair Queueing (WFQ) and Priority Queue (PQ).

关键词： Scheduler Priority Thresholds multi-class Quality of Service (QOS)

A primal perspective for indefinite kernel SVM problem

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Frontiers of Computer Science 2020年第2期14卷 349-363页

作者： Hui XUE Haiming XU Xiaohong CHEN Yunyun WANG School of Computer Science and Engineering Southeast UniversityNanjing 210096China Key Laboratory of Computer Network and Information Integration(Southeast University) MOENanjing 210096China College of Science Nanjing University of Aeronautics and AstronauticsNanjing 210016China School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210046China

Indefinite kernel support vector machine(IKSVM)has recently attracted increasing attentions in machine learning.Since IKSVM essentially is a non-convex problem,existing algorithms either change the spectrum of indefinite kernel directly but risking losing some valuable information or solve the dual form of IKSVM whereas suffering from a dual gap problem.In this paper,we propose a primal perspective for solving the problem.That is,we directly focus on the primal form of IKSVM and present a novel algorithm termed as IKSVM-DC for binary and multi-class classification.Concretely,according to the characteristics of the spectrum for the indefinite kernel matrix,IKSVM-DC decomposes the primal function into the subtraction of two convex functions as a difference of convex functions(DC)programming.To accelerate convergence rate,IKSVM-DC combines the classical DC algorithm with a line search step along the descent direction at each iteration.Furthermore,we construct a multi-class IKSVM model which can classify multiple classes in a unified form.A theoretical analysis is then presented to validate that IKSVM-DC can converge to a local minimum.Finally,we conduct experiments on both binary and multi-class datasets and the experimental results show that IKSVM-DC is superior to other state-of-the-art IKSVM algorithms.

关键词： indefinite kernel support vector machine multi-class classification non-convex optimization

基于AdaBoost的不完整数据的信息熵分类算法

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计算机与现代化 2013年第9期 31-34页

作者：吕靖舒礼莲安徽大学计算机科学与技术学院安徽合肥230601 江西省计算技术研究所江西南昌330002

目前,针对不完整数据的集成分类算法没有考虑缺失属性之间的差异,在衡量各个子分类器的权值时仅仅考虑了数据集的大小以及包含属性的多少,并没有考虑各个数据子集之间属性的差异度。本文利用信息熵对各个子数据集的重要程度进行量化,进... 详细信息

目前,针对不完整数据的集成分类算法没有考虑缺失属性之间的差异,在衡量各个子分类器的权值时仅仅考虑了数据集的大小以及包含属性的多少,并没有考虑各个数据子集之间属性的差异度。本文利用信息熵对各个子数据集的重要程度进行量化,进而评估从该数据集构建出的分类器的权值,使得在最终的加权投票过程更加公平,最终结果更加准确。使用基于multi-class AdaBoost的集成分类算法,以BP算法为基础分类器,对来自UCI的数据集进行实验,实验结果表明该算法在一定程度上提高了不完整数据的分类精度。

关键词： multi-class AdaBoost 信息熵不完整数据集成分类