The security of wireless routers receives much attention given by the increasing security threats. In the era of Internet of Things, many devices pose security vulnerabilities, and there is a significant need to analyze the current security status of devices. In this paper, we develop WNV-Detector, a universal and scalable framework for detecting wireless network vulnerabilities. Based on semantic analysis and named entities recognition, we design rules for automatic device identification of wireless access points and routers. The rules are automatically generated based on the information extracted from the admin webpages, and can be updated with a semi-automated method. To detect the security status of devices, WNV-Detector aims to extract the critical identity information and retrieve known vulnerabilities. In the evaluation, we collect information through web crawlers and build a comprehensive vulnerability database. We also build a prototype system based on WNV-Detector and evaluate it with routers from various vendors on the market. Our results indicate that the effectiveness of our WNV-Detector, i.e., the success rate of vulnerability detection could achieve 95.5%.
Data aggregation is a fundamental and efficient algorithm to reduce the communication overhead and energy consumption in wireless sensor networks (WSNs). However, WSNs need both energy-efficient and privacy-preserving when being deployed in an unprotected area. In this paper, we propose an energy-efficient and privacy-preserving data aggregation algorithm CBDA (the chain-based data aggregation). In the CBDA, sensor nodes will be organized as a tree topology. The leaf nodes of the tree sequentially reconnect with each other to form many chain topologies. For assuring data privacy, after data gathering, (1) the tail nodes (the nodes which on the tail of chain) need to slice their sensing data into J fragments. One fragment is kept by themselves, and they distribute the J −1 data fragments to their neighbor nodes. (2) Each tail node will inject some fake fragments into its J −1 fragments to interfere with adversaries. The CBDA can achieve less energy consumption and higher aggregation accuracy during data aggregation. We perform a comprehensive simulation to make a comparison among the CBDA with existing algorithms. The experimental results demonstrate that the CBDA outperforms the existing algorithms. INDEX TERMS Wireless sensor network, data processing, data privacy; energy conservation, accuracy.
Plant identification via leaf images is very meaningful to agricultural information. The existing methods were based on one or two kinds of the three distinct characteristics in leaf images including leaf contours, textures and veins. This limits their recognition performance and scope of application. This paper describes a novel counting-based leaf recognition method, which can directly and effectively combine all of the three kinds of significant characteristics in leaf images. In order to obtain the stable and independent local line responses from leaf contour, texture and vein, elliptical half Gabor is introduced and convoluted with the raw grayscale leaf images, and then maximum gap local line direction patterns are extracted from the local line responses and normalized in direction by cyclically right shifting these patterns until the most numerous bit plane with a value of 1 to the left bit. The histogram of the normalized patterns is calculated and regarded as the counting-based local structure descriptor, and support vector machine is utilized as the classifier. Experimental results on three frequently used leaf databases show that the proposed approach yields a better performance in terms of the classification accuracy, applicability and feasibility in comparison with the state of the art methods.
INDEX TERMSCounting-based descriptor, elliptical half-Gabor filters, maximum gap local line direction pattern, plant identification, support vector machine.
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