Novel approach for security in Wireless Sensor Network using bio-inspirations

TL;DR: While it uses machine learning techniques to identify the fraudulent nodes, consecutively by deriving inspiration from human immune system it effectively nullify the impact of the fraudulent ones on the network.

Abstract: Exploring the symbiotic nature of biological systems can result in valuable knowledge for computer networks. Biologically inspired approaches to security in networks are interesting to evaluate because of the analogies between network security and survival of human body under pathogenic attacks. Wireless Sensor Network (WSN) is a network based on multiple low-cost communication and computing devices connected to sensor nodes which sense physical parameters. While the spread of viruses in wired systems has been studied in-depth, applying trust in WSN is an emerging research area. Security threats can be introduced in WSN through various means, such as a benevolent sensor node turning fraudulent after a certain period of time. The proposed research work uses biological inspirations and machine learning techniques for adding security against such threats. While it uses machine learning techniques to identify the fraudulent nodes, consecutively by deriving inspiration from human immune system it effectively nullify the impact of the fraudulent ones on the network. Proposed work has been implemented in LabVIEW platform and obtained results that demonstrate the accuracy, robustness of the proposed model.

Summary

I. I NTRODUCTION

• Inspired by intrinsic appealing characteristics of biological systems, many researchers are engaged in producing novel design paradigms to address challenges in current network systems [1] .
• Biological inspired approaches seem promising since they are capable to self adapt, self heal, self organise in varying environmental conditions [2] .
• Over the recent years, there has been a paradigm shift in the development of computer networks; from monolithic, centralised systems to independent, distributed, self organised systems such as Wireless Sensor Networks (WSN).
• This also makes them prone to various types of attacks [10] .
• It also describes the human immune systems and explains the concept of T-cells and B-cells in their system.

II. R ELATED W ORK

• WSN can be considered as living beings usually born in a controlled environment, where all its nodes are cells that work selflessly towards a common goal.
• Detection of such fraudulent nodes become mandatory in such type of networks.
• In literature there are many trust models developed such as weightings method, artificial neural network method, swarm intelligence method etc [15] .
• Random data was generated between 10 and 20 for each of the sensor node and its deviation from mean value of past history was checked and if its variation is greater than 0.
• Figure 2 shows the result depicting rate of change of weights.

B. Artificial Neural Network

• Artificial Neural Network (ANN) approach in WSN calcu lates trust value based on present values as well as past history of neighbouring nodes.
• Based on the actual value received from the selected sensor node and the predicted value from estimation and prediction block, trust ratings are generated.
• Dark a way that lesser the variation between the predicted value and the actual value higher the trust rating and vice versa.

C. Swarm Intelligence

• Swarm Intelligence can be defined as collection of social insects and animals which can be represented by spatial arrangement and synchronized motion of individuals.
• In ant colony optimization technique, each of the ant deposit pheromone while traversing for the shortest path [19] .
• Shortest path was computed between the source and destination using Dijkstra's algorithm, showing the nodes in between the path as the trustworthy nodes.
• 4) Identify the critical or border line area by anomaly detection algorithm.
• 7) Finally gateway would be turning off the sensor node based upon the virtual antibody values.

A. Machine Learning Module

• Machine learning is one of the Intrusion Detection Sys tem(IDS) which checks the network traffic and decides whether these are symptoms of an attack or not [16] , [25] .
• Machine learning techniques develop algorithms for making predictions from data, to develop a model for accomplishing a particular task.
• Repeat the above two steps till convergence.
• Jh, f.-L2 f.-L K, 3) Anomaly Detection Engine: SVM creates the decision boundary however the data which lies on the boundary needs to be further evaluated for better accuracy and precision.
• Equations 4 and 5 are used to compute the mean/average and standard deviation of the benevolent data points respectively.

Mathematical Model

• Dibrov Model consists of coupled equations for the antibody quantity a, the antigen quantity g, and the small B cell population x [13] .
• H(t) in Equation 9is the Heaviside step function whose value is zero for negative argument and one for positive argument.
• The simplest assumption is that of the law of mass action, valid when the densities are below a saturation level, that is that the losses are proportional to the product of the antibody and antigen densities.
• The rate constants Q and R are necessarily not same.
• When simulations were carried out using the Runge Kutta method for solving the differential equations, following results were seen as shown in Figure 11 .

IV. E XPERIMENTS AND R ESULTS

• In the Immune Module, Dibrov Model was the basis for virtual antibodies production analogous to antibody production by B-cells in human immune system.
• This is done by assigning weights to the measurement values and these weights are proportional to the antigen values.
• Hence after the node becomes malicious, there is need to turn off the malicious node.
• This particular thing is called ON-OFF attack [7] .
• In that case increasing the sampling interval would be beneficial.

A. Non Weighted Averaging

• In non weighted averaging the measurement readings would be varied according to the noise in the malicious node.
• The variance from the true measurement is also varied.

B. Weighted Averaging

• Here weights are made proportional to the antigen value taken from the differential equation.
• Hence the measurements would be calculated as per the following equation.

e. Weighted Averaging and increasing sampling interval

• In weighted averaging + increase in sampling interval, good measurements would persist its state on true measurement for longer duration making the lifetime of malicious node longer.
• The sampling interval is increased by taking into account the antibodies value from differential equation.

D. Weighted Averaging and decrease sampling interval

• In the fourth scenario where sampling interval is decreased, variance from true measurements would be high leading to make malicious node lifetime very less.
• And when sampling interval is changed, sampling interval of other channels would also be affected.

E. Results comparing different options

• As seen from Figure 14 following tabular result can be formulated.
• Variance from true measurement is less in case of weighted averaging and increase in sampling interval.
• The impact on other channels only happen in the last two cases.
• The authors can choose to increase or decrease the sampling interval based upon the application they are using.
• And if the authors want to still monitor the malicious node after the detection as well then they can increase the sampling interval.

V. C ONCLUSION AND F UTURE S COPE

• This paper described the human immune system, specifi cally focussing on the adaptive immune system consisting of the T-cells and B-cells.
• Aim was to derive inspiration from these cells to design a security system for next generation wireless sensor network (WSN).
• Various trust models have been developed for the same.
• Where machine module is used for the detection of the fraudulent nodes; immune module is used for the removal of those nodes taking into account the antigen and antibody concept.

Full text

Novel Approach for Security in Wireless Sensor
Network using Bio-Inspirations
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          
      
  

       
     
     
       

         
           
 
  
  
         



        

  
 


  
         

     
        
         
        
 
   
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


        
      
  

         
 

        

         
         

        
        
       
          

         
         

          
        
         
         
 
       
         


  

         
         
 

     
       

  

 


          
        
        
 


   
 
  


     

          
           
          
 
   

         


  


         
      
         
         
    
 
           









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
 

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
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


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

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


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
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 

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


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
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     


    

      







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
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 
    

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 




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





 



 






     
  
   
 
       
        
      
      
         
        
       
       

        

         
        
        
         
      
          
          
         
           
 
     
 
     
         
      

      
         

        

  
   
   
       
        

         
        
       
 
         

    
 
   
  
          
         
   

        

        

         
   
  
        
       
          
      
        
         

          
        
         
       

   

  

 

       

    
          

  
  


 











  



 






      



 
   


        


   

       
         




























  


 


     
 


   



      
       

         
 

        

        

      
    
       
 
        
        

          
           
        
      
   
       
         





 














 
 






    
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
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 
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

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 
 

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 
   
 
   
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     
          
  
 




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V
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1\





        
    
 













     
  
     


  
     
  
    

      
        
       
       

         
  
        
          
          
       

        
       
      
         
         


 
         
       
        
    
      
          
  

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 

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
    


       


  


 


        
           
      
          
          
         
          
         
           
           
 
       
      
 



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

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
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
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  
  
 


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

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    

   
        
     

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
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
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 
         

     
         


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
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       

         
 

    
        
         
        
       

        














         
   








  
 
      
       
       
         
       