AN EFFECTIVE STATISTICAL TRAFFIC PATTERN DISCOVERY SYSTEM FOR MANETS USING DISTRIBUTED SUPER NODE SELECTION MODEL
Publication Date : 01/03/2016
Communication anonymity is a critical issue in MANETs, which is generally classified as Source/destination anonymity and end-to-end relationship anonymity. In this paper a novel Statistical Traffic Pattern Discovery System (STPDS) is presented to address these issues. STPDS works passively to perform traffic analysis based on statistical characteristics of captured raw traffic. STPDS is capable of discovering sources, destinations, and end-to-end communication relations. Empirical studies demonstrate that STPDS achieves good accuracy in disclosing the hidden traffic patterns. STPDS is a complete attacking system that first identifies all source and destination nodes and then determines their relationship. In addition, the STPDS is extended as Generalized STPDS (GSTPDS) which 1) divides the entire network into multiple regions geographically; 2) deploys mobile node along the boundaries of each region to monitor the cross-component traffic; 3) treats each region as a supernode and use STPDS to figure out the sources, destinations, and end-to-end communication relations, 4) analyzes the traffic even when nodes are close to each other by treating the close nodes as a supernode; Though many encryption techniques are used in video streaming applications in MANETs, which is using MDC with selective encryption technique is proposed in this work for enhancing scalability and confidentiality.
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