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Hybrid Node Watching Technique based Dos Flooding Attack Detection in Wireless Sensor Network
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Intrusion detection is the most concentrated research issue in the wireless sensor network where presence of intrusion activities are most difficult to find where there is no centralized architecture to monitor. One of the most frequently found intrusion activities in wireless sensor network are Denial of Service (DoS) Flooding attacks. DoS flood attacks would send large volume of chunk messages to the end node in order to corrupt the functioning of the particular node. Some of the most important DoS flooding attacks that are found in the network are ICMP flood attack, Synchronous Flood attack, UDP Flood attack, and Web attacks. All these networks would send enormous amount of messages such internet control message packets, synchronous messages, UDO messages correspondingly to the web servers to collapse the normal functioning of them by consuming energy resources and so on. In the previous research works, Sybil attacks and DDoS attacks are detected and avoided by introducing the method namely Privacy Concerned Anonymous Authentication Method (PAAM). However these research methods reduced in its attack detection rate with the presence of DoS Flooding attacks. This is focused and resolved in this work by introducing a method namely Hybrid Node Watching Technique (HNWT). This research technique attempt to find the variation in the data’s and control messages transmitted between the end nodes to find the flooding attack presence. This is done through the trust nodes which are selected optimally by using cat swarm algorithm. These optimally selected nodes will monitor data transmission behaviour to predict malicious node presence. The overall implementation of this research work is done in NS2 simulation environment from which it is proved that proposed research technique tends to have increased attack detection rate.
Keywords
Intrusion Attacks, DoS Flooding Attacks, Node Monitoring, ICMP Flood Attacks, Syn Flood Attack, UDP Flood Attack, Web Attacks.
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