Standard Self-organizing Maps Research Articles

MSOM: Efficient Mechanism for Defense against DDoS Attacks in VANET

The wireless nature of the Vehicular Ad Hoc Network (VANET), a technology that offers facilities such as traffic management and safety services, makes it vulnerable to distributed denial-of-service (DDoS) attacks that exploit network communications and reduce network reliability and performance. This paper proposes a design of a secure VANET architecture using a Software-Defined Networking (SDN) controller and Neural Network Self-Organizing Maps (SOMs). In the proposed design, we adopt the SDN architecture by using its separation of the control plane from the data plane and adding intelligent capabilities to the VANET. To resolve the drawbacks of standard SOMs and to enhance the SOM’s efficiency, a Multilayer Distributed SOM (MSOM) model based on two levels of clustering and classification is used. Experimental results show that our solution can efficiently detect malicious traffic, prevent and mitigate DDoS attacks, and increase system security and recovery speed from the attacking traffic. Moreover, the proposed scheme achieves a high accuracy rate (99.67%). Simulation results demonstrate the effectiveness and efficiency of the MSOM regarding detection accuracy and other studied metrics.

Topology-based analysis of self-organizing maps for time series prediction

A self-organizing map (SOM) is a competitive learning neural network architecture that uses a set of classificatory neurons, which self-organize spatially based on input patterns. The application of standard SOM, and its variants, to time series prediction has been studied in the literature under different conditions and techniques. In this paper, we have focused on the standard Self-Organizing Maps, and its application for time series forecasting. We provide extensive numerical analysis considering regular and complex topologies, and real time series. Concretely, we study the impact of the number of neurons, the effect of the best-matching unit over its neighborhood, the use of nonlinear learning rate functions, and the importance of a proportional training together with a sampled input space as uniformly as possible. We have found that probabilistic updating in neighborhoods may be used as a second learning rate parameter within the SOM. We have also found that small-world and scale-free topologies are able to improve the error of regular lattices, depending on their own set of parameters, the number of neurons, and the length of the training set. Finally, we have found coherence between results of real time series and analytic benchmarks for forecasting.

EFFECTIVE CLUSTERING ALGORITHM FOR HIGH-DIMENSIONAL SPARSE DATA BASED ON SOM

With increasing opportunities for analyzing large data sources, we have noticed a lack of effective processing in datamining tasks working with large sparse datasets of high dimensions. This work focuses on this issue and on effective clustering using models of artificial intelligence. The authors of this article propose an effective clustering algorithm to exploit the features of neural networks, and especially Self Organizing Maps (SOM), for the reduction of data dimensionality. The issue of computational complexity is resolved by using a parallelization of the standard SOM algorithm. The authors have focused on the acceleration of the presented algorithm using a version suitable for data collections with a certain level of sparsity. Effective acceleration is achieved by improving the winning neuron finding phase and the weight actualization phase. The output presented here demonstrates sufficient acceleration of the standard SOM algorithm while preserving the appropriate accuracy.

Prediction of liquid chromatographic retention behavior based on quantum chemical parameters using supervised self organizing maps

Self organizing maps (SOMs) in a supervised mode were applied for prediction of liquid chromatographic retention behavior of chemical compounds based on their quantum chemical information. The proposed algorithm was simple and required only a small alteration of the standard SOM algorithm. The application was illustrated by the prediction of the retention indices of bifunctionally substituted N-benzylideneanilines (NBA) and the prediction of the retention factors of some pesticides. Although the predictive ability of the supervised SOM could not be significantly greater than that of some previously established neural network methods, such as a radial basis function (RBF) neural network and a back-propagation artificial neural network (ANN), the main advantage of the proposed method was the ability to reveal non-linear structure of the model. The complex relationships between samples could be visualized using U-matrix and the influence of each variable on the predictive model could be investigated using component planes—which can provide chemical insight.

Neural maps in remote sensing image analysis

We study the application of self-organizing maps (SOMs) for the analyses of remote sensing spectral images. Advanced airborne and satellite-based imaging spectrometers produce very high-dimensional spectral signatures that provide key information to many scientific investigations about the surface and atmosphere of Earth and other planets. These new, sophisticated data demand new and advanced approaches to cluster detection, visualization, and supervised classification. In this article we concentrate on the issue of faithful topological mapping in order to avoid false interpretations of cluster maps created by an SOM. We describe several new extensions of the standard SOM, developed in the past few years: the growing SOM, magnification control, and generalized relevance learning vector quantization, and demonstrate their effect on both low-dimensional traditional multi-spectral imagery and ∼200-dimensional hyperspectral imagery.

Image denoising using self-organizing map-based nonlinear independent component analysis

This paper proposes the use of self-organizing maps (SOMs) to the blind source separation (BSS) problem for nonlinearly mixed signals corrupted with multiplicative noise. After an overview of some signal denoising approaches, we introduce the generic independent component analysis (ICA) framework, followed by a survey of existing neural solutions on ICA and nonlinear ICA (NLICA). We then detail a BSS method based on SOMs and intended for image denoising applications. Considering that the pixel intensities of raw images represent a useful signal corrupted with noise, we show that an NLICA-based approach can provide a satisfactory solution to the nonlinear BSS (NLBSS) problem. Furthermore, a comparison between the standard SOM and a modified version, more suitable for dealing with multiplicative noise, is made. Separation results obtained from test and real images demonstrate the feasibility of our approach.

Local linear independent component analysis based on clustering.

In standard Independent Component Analysis (ICA), a linear data model is used for a global description of the data. Even though linear ICA yields meaningful results in many cases, it can provide a crude approximation only for general nonlinear data distributions. In this paper a new structure is proposed, where local ICA models are used in connection with a suitable grouping algorithm clustering the data. The clustering part is responsible for an overall coarse nonlinear representation of the data, while linear ICA models of each cluster are used for describing local features of the data. The goal is to represent the data better than in linear ICA while avoiding computational difficulties related with nonlinear ICA. Several data grouping methods are considered, including standard K-means clustering, self-organizing maps, and neural gas. Connections to existing methods are discussed, and experimental results are given for artificial data and natural images. Furthermore, a general theoretical framework encompassing a large number of methods for representing data is introduced. These range from global, dense representation methods to local, very sparse coding methods. The proposed local ICA methods lie between these two extremes.