Conference Paper

The Incremental Probabilistic Neural Network

DOI: 10.1109/ICNC.2010.5583589 Conference: Sixth International Conference on Natural Computation, ICNC 2010, Yantai, Shandong, China, 10-12 August 2010
Source: DBLP


With the development of the Internet, the Intrusion Detection has been gradually playing a more and more important role in Network Security. Radial Basis Function Neural Network are widely used in Intrusion Detection, especially Probabilistic Neural Network. However, the detection speed is a problem which impedes it to be applied to Real-time Intrusion Detection. In this paper, for increasing the Detection Speed, the Incremental Training Method replaces the Exact Training Method. The simulation experiment shows that the detection speed of Incremental Probabilistic Neural Network is much faster than that of Exact Probabilistic Neural Network. Therefore, the Incremental Probabilistic Neural Network is more suitable for real-time intrusion detection than Exact Probabilistic Neural Network.

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    ABSTRACT: One critical challenge for accurate localization with Received Signal Strength Indicator (RSSI) is the anisotropic environment, which causes the RSS-Distance Relationship (RDR) to vary spatially. To alleviate localization error caused by RDR anisotropy, most of existing works adopt multiple RDR algorithms. However, we have found that the arbitrary RDR selection in these algorithms can lead to large localization error. Moreover, localization accuracy can be further enhanced by utilizing information provided by more Access Points (APs). To address these problems, we propose a Probabilistic Neural Network based localization algorithm in this paper. The algorithm features two steps: Global Optimization and Regional Compensation, during which all APs exchange information about the Blind Node (BN) to locate it collaboratively. Simulation result shows that the proposed algorithm can achieve a localization accuracy 35% higher than that of multiple RDR algorithms.
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