The Neighbor Discovery Protocol (NDP) is one of the main protocols in the Internet Protocol version 6 (IPv6) suite, and it provides many basic functions for the normal operation of IPv6 in a local area network (LAN), such as address autoconfiguration and address resolution. However, it has many vulnerabilities that can be used by malicious nodes to launch attacks, because the NDP messages are easily spoofed without protection. Surrounding this problem, many solutions have been proposed for securing NDP, but these solutions either proposed new protocols that need to be supported by all nodes or built mechanisms that require the cooperation of all nodes, which is inevitable in the traditional distributed networks. Nevertheless, Software-Defined Networking (SDN) provides a new perspective to think about protecting NDP. In this paper, we proposed an SDN-based authentication mechanism to verify the identity of NDP packets transmitted in a LAN. Using the centralized control and programmability of SDN, it can effectively prevent the spoofing attacks and other derived attacks based on spoofing. In addition, this mechanism needs no additional protocol supporting or configuration at hosts and routers and does not introduce any dedicated devices.
Hand gesture recognition that has proven a significant factor to directly influence the nonverbal communication between human and computer is becoming a challenging topic in the field of machine vision. This paper aims to propose a novel hand gesture recognition system which applies sparse representation to the Kinect to improve the efficiency of Kinect-based human-computer interaction. Auto-encoder neural network computation is also utilized to achieve better result. The sparse auto-encoder neural network is versatile and robust in complex features learning and computational efficient. Finally, results indicate that our algorithm greatly facilitates the gesture recognition rate up to 95%.
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