Denial of Service (DoS) attack is one of the popular attacks that occur in the sensor nodes of the Wireless Sensor Network (WSN). Therefore, to prevent this attack we propose DoS Attack Prevention Techniques in WSN Networks through which we can enhance the accuracy rate of attack prevention and minimize the false alarm. The existing system has the disadvantage of lack of learning capabilities and hence it has to be updated. Two learning parameters are added to the fuzzy system in the proposed work which improves the existing cooperative fuzzy artificial immune system (Co-FAIS).
Figure depicts the proposed immune system to prevent DoS attack on wireless sensor networks. There are two fuzzy modules in the proposed system. The number of learning parameters is altered. Throughput and sleep interval are the two learning parameters that are added to the existing system. Sniffer module grabs the packets and transmits them to the Fuzzy misuse detection module for preprocessing. It also creates log file of packets. Fuzzy misuse detector module is used to identify the malicious packet. Danger detector module calculates the difference between the parameters of malicious packet and normal packet. The actual attacks are observed in the fuzzy Q vaccination module. Cooperative decision-making module merges the output of FMDM and FQVM. Response module updates the database or alters the hosts of the network.
PhD projects in DoS Attack Prvention Techniques in Wireless Sensor Networks
Problem Statement
PhD projects in DoS Attack Prvention Techniques in Wireless Sensor Networks
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