Abstract. Combining long sequences of overlapping depth maps without simplification results in a huge number of redundant points, which slows down further processing. In this paper, a novel method is presented for incrementally creating a nonredundant point cloud with varying levels of detail without limiting the captured volume or requiring any parameters from the user. Overlapping measurements are used to refine point estimates by reducing their directional variance. The algorithm was evaluated with plane and cube fitting residuals, which were improved considerably over redundant point clouds.

Intrusion prevention systems have become a common security measure in the past 20 years. Their promise is the possibility to prevent known attacks against vulnerable, unpatched devices inside enterprise networks. However, evasion techniques that enable the attacker to evade the eye of the intrusion prevention system are a potential problem for this capability. These techniques take advantage of the robustness principle that has guided designers to create systems that will try to recreate protocol content from any input they receive.In this work, we evaluated the effectiveness of 35 well-known evasions against 9 commercial and 1 free, state-of-the-art, intrusion prevention systems. We conducted 4 experiments with one million attacks against each device. Each system lets a significant amount (0.1%-45%) of attacks pass through unrecognized. Our results show that most existing intrusion prevention systems are vulnerable against evasions.