基于SOM网络的智能入侵检测系统

随着网络技术的不断发展,入侵检测技术作为安全防护的重要手段,显得日益重要.针对现有入侵检测系统识别率低、误报率高的问题,将SOM神经网络结合Agent技术应用到入侵检测系统.结合模糊逻辑的思想对SOM网络的学习算法进行了模糊化改进,利用SOM网络的自组织特性对网络数据流量强度进行建模和聚类;使用Agent技术进行实时监控网络环境的安全状况、入侵企图的识别等.提出一个基于自组织神经网络的智能入侵检测模型,描述了模型体系结构及其工作流程.通过实验进行仿真,实验证明系统有较好的识别率和较低的误报率.     

一种协同半监督分类算法Co-S3OM

为了提高半监督分类的有效性, 提出了一种基于SOM神经网络和协同训练的半监督分类算法Co-S3OM (coordination semi-supervised SOM)。将有限的有标记样本分为无重复的三个均等的训练集, 分别使用改进的监督SSOM算法(supervised SOM)训练三个单分类器, 通过三个单分类器共同投票的方法挖掘未标记样本中的隐含信息, 扩大有标记样本的数量, 依次扩充单分类器训练集, 生成最终的分类器。最后选取UCI数据集进行实验, 结果表明Co-S3OM具有较高的标记率和分类率。     

基于人工神经网络的地震电场卫星数据异常识别

为实现地震卫星异常数据的快速查找,本文提取了汶川地震发生前10天的ULF频段电场波形数据,以均值、均方差、峰度和偏度作为特征信息,设计了BP神经网络分类器,并通过SOM网聚类模型进行验证。计算结果表明,异常区域位于汶川地震震中以南的较大区域范围内,可能是由于汶川南部地震波能量巨大引起较强的空间电离层扰动;BP网分类器的正常数据识别率达到98.13%,异常数据的识别率为96.75%;SOM网聚类分析结果显示,与BP网络分类器结果具有较好一致性。     

基于SOM的入侵检测方法研究

针对现有入侵检测系统识别率低、误报率高的问题,将SOM神经网络应用到入侵检测系统。自组织特征映射神经网络SOM(Self Organizing Feature Maps)作为一种优良的聚类工具,具有无需监督,能自动对输入模式进行聚类的优点。为验证检测方法的有效性,采用KDDCup99的训练集与测试集进行实验。     

自组织映射神经网络在入侵检测中的应用

提出了一种基于SOM神经网络的入侵检测方法。该方法采用有标签的数据训练SOM神经网络,然后根据训练的结果标记正常数据和异常数据聚类的神经元。检测时则根据被检测数据的最佳匹配神经元的标签判断攻击是否发生。为验证检测的有效性,采用KDD cup99的训练集与测试集,将基于SOM的检测方法与基于SVM的检测方法的检测效果做了对比。实验结果表明：基于SOM的入侵检测方法具有检测率高、训练时间短和通用性强等特点。     

基于集成学习的入侵检测方法

入侵检测是近年来网络安全研究的热点。利用多分类器技术,研究了基于集成学习的入侵检测方法。应用Bootstrap技术生成分类器个体,为了提高分类器的差异性,应用聚类技术对分类器进行聚类,在相应的聚类结果中选取不同的分类器个体,并选择不同的融合方法对分类结果进行融合。针对入侵检测数据的实验表明了该集成技术的有效性。     

一种基于部分标记的SOM和MLP混合识别算法

针对孟加拉手写体数字的识别，本文提出了一种基于部分标记的两层SOM分类器和MLP分类器相混合的识别算法。对于Kohonen提出的SOM分类器，部分标记有利于降低错识率，而两层结构提高了分类器的性能。在本文提出的算法中，首先用SOM分类器对提取的字符方向特征和密度特征进行处理，如果字符不能被SOM识别，则再用MLP分类器对其重新分类。文章最后用实际信封上提取的16，000个孟加拉数字进行了实验，实验证明，本混合算法达到了96．7％的识别率。     

基于k-means聚类的神经网络分类器集成方法研究

针对差异性是集成学习的必要条件,研究了基于k-means聚类技术提高神经网络分类器集成差异性的方法。通过训练集并使用神经网络分类器学习算法训练许多分类器模型,在验证集中利用每个分类器的分类结果作为聚类的数据对象;然后应用k-means聚类方法对这些数据聚类,在聚类结果的每个簇中选择一个分类器代表模型,以此构成集成学习的成员;最后应用投票方法实验研究了这种提高集成学习差异性方法的性能,并与常用的集成学习方法bagging、adaboost进行了比较。     

SOM神经网络模型在商业银行信用风险评估中的应用研究

研究了神经网络技术在商业银行信用风险评估中的应用,结合主成分分析法和SOM人工神经网络,建立了商业银行信用风险评估的人工神经网络模型;实证结果表明,该模型具有较高的预测精度.     

基于BP神经网络的交通标志识别

对神经网络理论和神经网络分类器进行了研究,提出了基于BP神经网络分类器的交通标志识别模型。通过大量实验和比较,得到了识别效率高的模型,并将这一模型应用到所研究的交通标志识别系统,从而对系统作了初步的实现。     

Nonlinear Dimensionality Reduction and Data Visualization： A Review

Dimensionality reduction and data visualization are useful and important processes in pattern recognition.Many techniques have been developed in the recent years.The self-organizing map (SOM) can be an efficient method for this purpose.This paper reviews recent advances in this area and related approaches such as multidimensional scaling (MDS),nonlinear PCA,principal manifolds,as well as the connections of the SOM and its recent variant,the visualization induced SOM (ViSOM),with these approaches. The SOM is shown to produce a quantized,qualitative scaling and while the ViSOM a quantitative or metric scaling and approximates principal curve/surface.The SOM can also be regarded as a generalized MDS to relate two metric spaces by forming a topological mapping between them.The relationships among various recently proposed techniques such as ViSOM,Isomap,LLE,and eigenmap are discussed and compared.     

基于多层自组织映射和主成分分析的入侵检测方法*

首先改进了自组织映射学习和分类算法,通过引入自定义变量匹配度、约简率和约简样本量化误差,提出了一种新的基于多层自组织映射和主成分分析入侵检测模型与算法。模型运用主成分分析算法对输入样本进行特征约简,运用分层思想对分类精度低的聚类进行逐层细分,解决了单层自组织映射分类不精确的问题。实验结果表明该模型用于入侵检测的效果良好,能准确区分攻击与否且能进一步指出攻击的具体类型。     

An intelligent intrusion detection system (IDS) for anomaly and misuse detection in computer networks

In this paper, we propose a novel Intrusion Detection System (IDS) architecture utilizing both anomaly and misuse detection approaches. This hybrid Intrusion Detection System architecture consists of an anomaly detection module, a misuse detection module and a decision support system combining the results of these two detection modules. The proposed anomaly detection module uses a Self-Organizing Map (SOM) structure to model normal behavior. Deviation from the normal behavior is classified as an attack. The proposed misuse detection module uses J.48 decision tree algorithm to classify various types of attacks. The principle interest of this work is to benchmark the performance of the proposed hybrid IDS architecture by using KDD Cup 99 Data Set, the benchmark dataset used by IDS researchers. A rule-based Decision Support System (DSS) is also developed for interpreting the results of both anomaly and misuse detection modules. Simulation results of both anomaly and misuse detection modules based on the KDD 99 Data Set are given. It is observed that the proposed hybrid approach gives better performance over individual approaches.     

Self-organizing map network as an interactive clustering tool — An application to group technology

The Self-Organizing Map (SOM) network, a variation of neural computing networks, is a categorization network developed by Kohonen. The theory of the SOM network is motivated by the observation of the operation of the brain. This paper presents the technique of SOM and shows how it may be applied as a clustering tool to group technology. A computer program for implementing the SOM neural networks is developed and the results are compared with other clustering approaches used in group technology. The study demonstrates the potential of using the Self-Organizing Map as the clustering tool for part family formation in group technology.     

Multiple-fault detection and identification scheme based on hierarchical self-organizing maps applied to an electric machine

Strategies of condition monitoring applied to electric motors play an important role in the competitiveness of multiple industrial sectors. However, the risk of faults coexistence in an electric motor and the overlapping of their effects in the considered physical magnitudes represent, currently, a critical limitation to provide reliable diagnosis outcomes. In this regard, additional investigation efforts are required towards high-dimensional data fusion schemes, particularly over the features calculation and features reduction, which represent two decisive stages in such data-driven approaches. In this study, a novel multiple-fault detection and identification methodology supported by a feature-level fusion strategy and a Self-Organizing Maps (SOM) hierarchical structure is proposed. The condition diagnosis as well as the corresponding estimated probability are obtained. Moreover, the proposed method allows the visualization of the results while preserving the underlying physical phenomenon of the system under monitoring. The proposed scheme is performed sequentially; first, a set of statistical-time based features is estimated from different physical magnitudes. Second, a hybrid feature reduction method is proposed, composed by an initial soft feature reduction, based on sequential floating forward selection to remove the less informative features, and followed by a hierarchical SOM structure which reveals directly the diagnosis and probability assessment. The effectiveness of the proposed detection and identification scheme is validated with a complete set of experimental data including healthy and five faulty conditions. The accuracy’s results are compared with classical condition monitoring approaches in order to validate the competency of the proposed method.     

一种有效的可视化孤立点发现与预测新途径

孤立点发现是数据挖掘活动的重要组成部分,被广泛应用于电子贸易、信用卡等领域的欺诈检测。由于优良的拓扑结构保持和概率分布保持特性,SOM（Self-Organizing Maps）可作为一种有效的降维工具供分析人员获取隐藏于数据中的分布结构信息。在分析了当前基于距离的孤立点发现的基础上,提出了一种基于SOM的孤立点发现与预测新途径,具有可扩展性、可预测性、交互性、简明性等特征。实验结果表明,基于SOM的孤立点发现与预测是有效的。     

A survey on service-oriented middleware for wireless sensor networks

Wireless sensor networks (WSN) are used for many applications such as environmental monitoring, infrastructure security, healthcare applications, and traffic control. The design and development of such applications must address many challenges dictated by WSN characteristics on one hand and the targeted applications on the other. One of the emerging approaches used for relaxing these challenges is using service-oriented middleware (SOM). Service-oriented computing, in general, aims to make services available and easily accessible through standardized models and protocols without having to worry about the underlying infrastructures, development models, or implementation details. SOM could play an important role in facilitating the design, development, and implementation of service-oriented systems. This will help achieve interoperability, loose coupling, and heterogeneity support. Furthermore, SOM approaches will provision non-functional requirements like scalability, reliability, flexibility, and Quality of Service (QoS) assurance. This paper surveys the current work in SOM and the trends and challenges to be addressed when designing and developing these solutions for WSN.     

Slippage and immobilization detection for planetary exploration rovers via machine learning and proprioceptive sensing

This paper presents a new methodology where machine learning is used for detecting various levels of slip in the context of planetary exploration robotic missions. This methodology aims at employing proprioceptive rover sensor signals. Consequently, no operational complexity is added to the rover's commanding and it is independent of lighting conditions. Two supervised learning methods (Support Vector Machines and Artificial Neural Networks) are compared to two unsupervised learning approaches (K‐means and Self‐Organizing Maps (SOM)). Physical experiments using a single‐wheel testbed equipped with an MSL spare wheel and a real planetary exploration rover validate the implemented methodology. Performance is evaluated in terms of well‐known metrics both considering single data points and subsets of consecutive data points (moving median filter). Computation time and storage requirements are also examined. One of the SOM‐based algorithms, semantic SOM method, demonstrates a proper balance between the benefits of supervised learning algorithms (high success rate, >96%) and the advantages of unsupervised learning methods (low storage requirements, 5 kb, and no need of manually‐labeled training data). This paper also addresses the most convenient placement of IMU sensors on the rover chassis such that slippage detection is maximized.