Denial of Service (DoS) attacks, and jamming in particular, present a significant threat to wireless networks because they are easy to mount and difficult to detect and prevent. We present and analyze a special type of DoS attack, called random packet destruction (RPD) that works by transmitting short periods of noise signals. The RPD DoS attack can effectively shut down a wireless network. Since the attacker does not need to pretend to be a legal user participating in the network, current anti-attack measures such as encryption, authentication and authorization cannot prevent these types of attacks. RPD DoS attacks are pervasive in nature and can potentially be launched against any wireless networks that are detectable. An attacker can launch RPD attacks against wireless networks used for mission critical systems to inflict serious damages on lives or properties. The paper presents for the first time, both theoretical analysis and performance simulations of WLANs when operating under RPD DoS attacks for a range of types of network traffic.

This paper studies the problem of how to assess the quality of photographing viewpoints and how to choose good viewpoints for taking photographs of architectures. We achieve this by learning from photographs of world famous landmarks that are available on the Internet and their viewpoint quality ranked by online user annotation. Unlike previous efforts devoted to photo quality assessment which mainly rely on 2D image features, we show in this paper combining 2D image features extracted from images with 3D geometric features computed on the 3D models can result in more reliable evaluation of viewpoint quality. Specifically, we collect a set of photographs for each of 15 world famous architectures as well as their 3D models from the Internet. Viewpoint recovery for images is carried out through an image-model registration process, after which a newly proposed viewpoint clustering strategy is exploited to validate users' viewpoint preferences when photographing landmarks. Finally, we extract a number of 2D and 3D features for each image based on multiple visual and geometric cues and perform viewpoint recommendation by learning from both 2D and 3D features using a specifically designed SVM-2K multi-view learner, achieving superior performance over using solely 2D or 3D features. We show the effectiveness of the proposed approach through extensive experiments. The experiments also demonstrate that our system can be used to recommend viewpoints for rendering textured 3D models of buildings for the use of architectural design, in addition to viewpoint evaluation of photographs and recommendation of viewpoints for photographing architectures in practice.

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Abstract. This paper introduces a novel Management Traffic Clustering Algorithm (MTCA) based on a sliding window methodology for intrusion detection in 802.11 networks Active attacks and other network events such as scanning, joining and leaving in 802.11 WLANs can be observed by clustering the management frames in the MAC Layer. The new algorithm is based on a sliding window and measures the similarity of management frames within a certain period by calculating their variance. Through filtering out certain management frames, clusters are recognized from the discrete distribution of the variance of the management traffic load. Two parameters determine the accuracy and robustness of the algorithm: the Sample Interval and the Window Size of the sliding window. Extensive tests and comparisons between different sets of Sample Intervals and Window Sizes have been carried out. From analysis of the results, recommendations on what are the most appropriate values for these two parameters in various scenarios are presented.

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