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Proceedings ArticleDOI

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 (3 min read)

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.

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

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

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

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

      
        
       
       

         
  
        
          
          
       

        
       
      
         
         


 
         
       
        
    
      
          
  


 




   


       


  


 


        
           
      
          
          
         
          
         
          
           
 
       
      
 



 










 


 
  
  
 







 
    

   
        
     
 
 
 



 
         

     
         




       

         
 

    
        
         
        
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Citations
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Proceedings ArticleDOI
01 Aug 2018
TL;DR: This paper surveys the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them.
Abstract: The Internet of Things (IoT) is the network where physical devices, sensors, appliances and other different objects can communicate with each other without the need for human intervention Wireless Sensor Networks (WSNs) are main building blocks of the IoT Both the IoT and WSNs have many critical and non-critical applications that touch almost every aspect of our modern life Unfortunately, these networks are prone to various types of security threats Therefore, the security of IoT and WSNs became crucial Furthermore, the resource limitations of the devices used in these networks complicate the problem One of the most recent and effective approaches to address such challenges is machine learning Machine learning inspires many solutions to secure the IoT and WSNs In this paper, we survey the different threats that can attack both IoT and WSNs and the machine learning techniques developed to counter them

77 citations


Cites methods from "Novel approach for security in Wire..."

  • ...The algorithm presented in [17] applied machine learning to discover if a benevolent node became a malicious one....

    [...]

Proceedings ArticleDOI
19 Mar 2015
TL;DR: The leading security and privacy issues are survey and potential attacks WBANs are surveyed and an unsolved problem is that quality service is a serious security issue has great potential for currency inWBANs is explained, and a potential future direction is discussed.
Abstract: Wireless body area sensor networks (WBANs) are becoming more popular and great potential of the human body is shown in real time monitoring. Cost-effective, unobtrusive and unsupervised continuous monitoring applications like surveillance, as well as the promise of WBANs Healthcare has attracted a wide range of activity and rehabilitation system game. However, the benefits of WBANs, using a number of challenging issues must be resolved. In addition to the open issues in standardization, WBANs energy efficiency and quality of service (QoS), security and privacy issues are major concerns. These wearable system life-critical data should be protected, since they control. Nevertheless, these systems face some difficulties of addressing security. WBANs wireless sensor networks (WSN) to inherit the most well known security challenges. However, WBANs, the specific symptoms such as severe resource constraints and harsh environmental conditions, security and privacy challenges the pretending to support additional unique. In this paper, survey the leading security and privacy issues and will survey potential attacks WBANs. In addition, we have an unsolved problem is that quality service is a serious security issue has great potential for currency in WBANs will explain, and then we discuss a potential future direction.

59 citations

Proceedings ArticleDOI
01 Sep 2016
TL;DR: In this paper, the authors studied the security issues and security threats in WSNs and gave brief description of some of the protocols used to achieve security in the network and compared the proposed methodologies analytically and demonstrates the findings in a table.
Abstract: Wireless Sensor Networks (WSNs) are formed by deploying as large number of sensor nodes in an area for the surveillance of generally remote locations. A typical sensor node is made up of different components to perform the task of sensing, processing and transmitting data. WSNs are used for many applications in diverse forms from indoor deployment to outdoor deployment. The basic requirement of every application is to use the secured network. Providing security to the sensor network is a very challenging issue along with saving its energy. Many security threats may affect the functioning of these networks. WSNs must be secured to keep an attacker from hindering the delivery of sensor information and from forging sensor information as these networks are build for remote surveillance and unauthorized changes in the sensed data may lead to wrong information to the decision makers. This paper studies the various security issues and security threats in WSNs. Also, gives brief description of some of the protocols used to achieve security in the network. This paper also compares the proposed methodologies analytically and demonstrates the findings in a table. These findings can be used further by other researchers or Network implementers for making the WSN secure by choosing the best security mechanism.

38 citations

Journal ArticleDOI
TL;DR: The state-of-the-art in nature-inspired computing and wake-up scheduling algorithms for wireless sensor networks is described and the most recent developments in this interdisciplinary domain are described.
Abstract: Over the last few decades, algorithms inspired by nature have matured into a widely used class of computing methods. They have shown the ability to adjust to variety of conditions, and have been frequently employed for solving complex, real-world optimization problems. They are especially suitable for problems that require adaptation, and that involve optimization of complex, distributed systems, operating in dynamic environments. Among other application domains, nature-inspired methods have been extensively used in the areas of networking in general, and wireless sensor networks in particular. Energy management and network lifetime optimization are two great research and implementation challenges for wireless sensor networks. Duty cycle management, synchronization, and wake-up scheduling are complementary approaches that facilitate this complex optimization process. This review focuses on the intersection of nature-inspired computing and wake-up scheduling algorithms for wireless sensor networks. It describes the state-of-the-art in these fields and provides an up-to-date review of the most recent developments in this interdisciplinary domain. It discusses the motivation for using nature-inspired methods for wake-up scheduling, and presents related open issues and research challenges.

36 citations

Journal ArticleDOI
23 Jun 2022-Sensors
TL;DR: The possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains is discussed, in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machineLearning algorithms.
Abstract: Energy and security are major challenges in a wireless sensor network, and they work oppositely. As security complexity increases, battery drain will increase. Due to the limited power in wireless sensor networks, options to rely on the security of ordinary protocols embodied in encryption and key management are futile due to the nature of communication between sensors and the ever-changing network topology. Therefore, machine learning algorithms are one of the proposed solutions for providing security services in this type of network by including monitoring and decision intelligence. Machine learning algorithms present additional hurdles in terms of training and the amount of data required for training. This paper provides a convenient reference for wireless sensor network infrastructure and the security challenges it faces. It also discusses the possibility of benefiting from machine learning algorithms by reducing the security costs of wireless sensor networks in several domains; in addition to the challenges and proposed solutions to improving the ability of sensors to identify threats, attacks, risks, and malicious nodes through their ability to learn and self-development using machine learning algorithms. Furthermore, this paper discusses open issues related to adapting machine learning algorithms to the capabilities of sensors in this type of network.

28 citations

References
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TL;DR: The concept of sensor networks which has been made viable by the convergence of micro-electro-mechanical systems technology, wireless communications and digital electronics is described.

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TL;DR: A fun and exciting textbook on the mathematics underpinning the most dynamic areas of modern science and engineering.
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TL;DR: Experimental results showing that employing the active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings are presented.
Abstract: Support vector machines have met with significant success in numerous real-world learning tasks. However, like most machine learning algorithms, they are generally applied using a randomly selected training set classified in advance. In many settings, we also have the option of using pool-based active learning. Instead of using a randomly selected training set, the learner has access to a pool of unlabeled instances and can request the labels for some number of them. We introduce a new algorithm for performing active learning with support vector machines, i.e., an algorithm for choosing which instances to request next. We provide a theoretical motivation for the algorithm using the notion of a version space. We present experimental results showing that employing our active learning method can significantly reduce the need for labeled training instances in both the standard inductive and transductive settings.

3,212 citations

Journal ArticleDOI
TL;DR: Two serial and parallel algorithms for solving a system of equations that arises from the discretization of the Hamilton-Jacobi equation associated to a trajectory optimization problem of the following type are presented.
Abstract: We present serial and parallel algorithms for solving a system of equations that arises from the discretization of the Hamilton-Jacobi equation associated to a trajectory optimization problem of the following type. A vehicle starts at a prespecified point x/sub o/ and follows a unit speed trajectory x(t) inside a region in /spl Rscr//sup m/ until an unspecified time T that the region is exited. A trajectory minimizing a cost function of the form /spl int//sub 0//sup T/ r(x(t))dt+q(x(T)) is sought. The discretized Hamilton-Jacobi equation corresponding to this problem is usually solved using iterative methods. Nevertheless, assuming that the function r is positive, we are able to exploit the problem structure and develop one-pass algorithms for the discretized problem. The first algorithm resembles Dijkstra's shortest path algorithm and runs in time O(n log n), where n is the number of grid points. The second algorithm uses a somewhat different discretization and borrows some ideas from a variation of Dial's shortest path algorithm (1969) that we develop here; it runs in time O(n), which is the best possible, under some fairly mild assumptions. Finally, we show that the latter algorithm can be efficiently parallelized: for two-dimensional problems and with p processors, its running time becomes O(n/p), provided that p=O(/spl radic/n/log n). >

816 citations