Smart devices are equipped with technology that facilitates communication among devices connected via the Internet. These devices are shipped with a user interface that enables users to perform administrative activiti...
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Smart devices are equipped with technology that facilitates communication among devices connected via the Internet. These devices are shipped with a user interface that enables users to perform administrative activities using a web browser linked to the device's server. Cross-site scripting (XSS) is the most prevalent web application vulnerability exploited by attackers to compromise smart devices. In this paper, the authors have designed a framework for shielding smart devices from XSS attacks. It is a machine learning-based attack detection framework which employs self-organizing-map (SOM) to classify XSS attack string. The input vector to the SOM is generated based on attack ontology and the changing behavior of the attack strings in different input fields in the device web interface. Additionally, it also sanitizes the injected attack string to neutralize the harmful effects of attack. The experimental results are obtained using the real-world dataset on the XSS attack. We tested the proposed framework on web interface of two smart devices (TP-link Wi-Fi router and HP color printer) containing hidden XSS vulnerabilities. The observed results unveil the robustness of the proposed work against the existing work as it achieves a high accuracy of 0.9904 on the tested dataset. It is a platform-independent attack detection system deployed on the browser or server side.
This article applies the method of artificial neural networks in the outlier detection, and gives one kind of new outlier detection method. According to the thoughts of the GHSOM algorithm and the GHTSOM algorithm, we...
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ISBN:
(纸本)9789881563835
This article applies the method of artificial neural networks in the outlier detection, and gives one kind of new outlier detection method. According to the thoughts of the GHSOM algorithm and the GHTSOM algorithm, we make the improvement to the SOM algorithm. The improvement algorithm can be applied in the outlier detection, and this dissertation gives the different outlier detection examples and analyze the algorithm performance and the expansion ability, the performance is quite stable and adaptation is quite strong to the different date. Compares with the outlier detection use support vector machines, this method doesn't need to choose the kernel function and adjust the parameter unceasingly, and it has very good adaptation to the change of the data distribution.
Uncertainty in oil sands reservoirs can be quantified by generating multiple realizations using geostatistical methods. However, it requires huge computing time to simulate all of the realizations. This article propos...
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Uncertainty in oil sands reservoirs can be quantified by generating multiple realizations using geostatistical methods. However, it requires huge computing time to simulate all of the realizations. This article proposes a new approach for features modeling of oil sands reservoirs in metric space. As the first step, an area affected by the expansion of a steam chamber is set and converted to the polar coordinate system. The converted area is expressed as an image matrix consisting of 0 or 1 value. Then the matrix is transformed using two-dimensional discrete Fourier transform. Key features in the front columns and rows of the transformed matrix are extracted. These features in metric space are plotted using principal component analysis. self-organizing map algorithm is used to select representative models of realizations for performing full flow simulations. In the result of grouping, each cluster group distributes separately in metric space according to reservoir productivity, but there are mixes of a small portion among the adjacent groups due to similar productivity.
With the development of information technology(IT), finding useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully appli...
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With the development of information technology(IT), finding useful information existed in vast data has become an important issue. The most broadly discussed technique is data mining, which has been successfully applied to many fields. Data mining extracts implicit, previously unknown, and useful information from data. Clustering analysis is one of the most important and useful technologies in data mining methods. In this research, we utilize clustering analysis to analyze user loyalty of mobile phone APP software and use RFM analysis to describe the user behavior. By using the self-organizing map algorithm, the proposed system is expected to provide marketing survey industry with precise market segmentation for marketing strategy decision making and extended applications. According to the market segmentation, special offers to the varied consumer group can stimulate purchasing behavior and finally improve click-and-mortar conversion rate effectively.
Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human per...
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ISBN:
(纸本)9781424496365
Data mining techniques are becoming indispensable as the amount and complexity of available data is rapidly growing. Visual data mining techniques attempt to include a human observer in the loop and leverage human perception for knowledge extraction. This is commonly allowed by performing a dimensionality reduction into a visually easy-to-perceive 2D space, which might result in significant loss of important spatial and topological information. To address this issue, this paper presents the design and implementation of a unique 3D visual data mining framework - CAVE-SOM. The CAVE-SOM system couples the self-organizingmap (SOM) algorithm with the immersive Cave Automated Virtual Environment (CAVE). The main advantages of the CAVE-SOM system are: i) utilizing a 3D SOM to perform dimensionality reduction of large multi-dimensional datasets, ii) immersive visualization of the trained 3D SOM, iii) ability to explore and interact with the multi-dimensional data in an intuitive and natural way. The CAVE-SOM system uses multiple visualization modes to guide the visual data mining process, for instance the data histograms, U-matrix, connections, separations, uniqueness and the input space view. The implemented CAVE-SOM framework was validated on several benchmark problems and then successfully applied to analysis of wind-power generation data. The knowledge extracted using the CAVE-SOM system can be used for further informed decision making and machine learning.
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