Secuirty framework for selfishness and selfishness oriented attacks at CBWSNs

I want to make a security framework for cluster-based wireless sensor network against the selfishness attack and selfishness oriented attacks that will use a distributed clustering algorithm and reputation system and data mining techniques such as Bayesian rule at sensor nodes and support vector machine at the sink node. We made the following assumptions ie.

WSNs consists of N sensors and one base station and each sensor has a list of neighbours that consists of M nodes

Selfishness attack: The node will be considered selfish if it drops packets according to the certain ratio as shown in the table. Where the intensity of attacks varies according to the dropping ratios

Table: Nodes Selfishness levels

Node Partial Selfishness Full Selfishness Physical Damage

Sn 30 %<PF<100% PF=<30 % PF=0

Sn= sensor node, PF= packet forwarding

Each Sensor node before clustering has an initial reputation values for its one hop away neighbours that will update from time to time according to their forwarding activity for it.

As already said that these reputation system using forwarding activity as their direct observation for updating the reputation value. But also analyses this forwarding activity record for a neighbour using Bayesian classifier to find if there is an attack or not and then send this data to the sink node.

We also assume to calculate integrated reputation value; we will use direct observation and indirect observation, where the indirect observation values come from the sink node having support vector machine classifier. Sink node will find common neighbours of evaluating and evaluated node and analyses their submitted direct observation using support vector machine and tried to find any attack existence and provide this result as indirect observation for evaluating node to calculate the total reputation values.

The overall reputation value OR

Habilidades: Programação C, Programação C++ , Machine Learning (ML), Matlab and Mathematica, Python

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ID do Projeto: #19540840