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.

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Browser extensions are third-party applications that can customize the browsing experience. Previous studies have shown that browser extension fingerprinting can be used to track users and reveal users’ privacy information by obtaining the browser extension list. However, the proposal of various defense measures weakens the effectiveness of the existing extension fingerprinting technologies. In this paper, we first propose two extension fingerprinting technologies: JavaScript-based environmental fingerprinting and DOM-based behavioral fingerprinting. They, respectively, capture the operation behaviors of extensions on JavaScript properties and webpage’s DOM. Second, we design BEFP, an extension recognition system which comprehensively utilizes the above two technologies to improve the uniqueness of the extension fingerprint. Finally, we collect the latest data set and carry out experiments on the actual scenario where users install multiple extensions. The results show that the true positive rate of extension recognition is as high as 96.3%. And the extension’s detectable rate of BEFP is superior to the existing technologies. Moreover, it is proved that the JavaScript-based environmental fingerprinting can complement the DOM-based fingerprinting to distinguish the extensions with the same DOM modification.