Secure Data Aggregation in WSN for minimizing Attacker’s ... · Secure Data Aggregation Technique...

Secure Data Aggregation in WSN forminimizing Attacker’s Impact

Mr.Kalyan Devappa Bamane1,Dr.Purohit Shrinivasacharya2

1,2 VTU RRC [email protected]

July 2, 2018

Abstract

Internet of Things is one of the platforms which is usedvery much in this modern age. Nowadays it becomes oneof the most important parts of future internet. IOT is alsocontaining the major constituent that is Wireless sensor net-work. In our Tree based Data aggregation for minimizingthe attackers impact, we are implementing the IOT in ourearthquake prone area so that we can sense the multipledata from various node and we are checking the system forvarious attack and we will finalize to minimize the attackersimpact .We use this mechanism of IOT to in our WSN net-works for increasing the lifetime of the network and also italso helps in the power consumption of the devices. In thispaper we used the sensor nodes for example load nodes formilitary area, motion nodes for earthquake.

1 Introduction:

Nowadays, in this internet and networking world, there are manyof the fast growth distributed and internet based systems such asInternet of Things (IoT), cloud computing, grid computing. Inour paper, we are using the IoT for connecting the sensor devices.Internet of Things is manly having meaning that connecting the

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many of devices to the internet as well as the devices each other.IoT is one of the big network which is connecting the peoples andalso things with the help of this it becomes very easy to collectand share or transfer data. This network is including the numberof objects which are having the different shapes and sizes. Sensordevices which are connecting to the Internet of Things platformthat are integrating the data to the devices which are connectedand also sharing the data. In the IoT , the devices which are con-nected through it are also the sensor devices. In this paper, we areusing the wireless sensor nodes connected to the internet and alsoeach other for transferring the data. In this system the informationwhich is transfer by the sensor nodes that are also transferring thedata to the cloud. In the IoT, with the help of different types of sen-sors and actuators, many of the massive devices can be found whichare connected to the internet with the help of the heterogeneous ac-cess network like the wireless sensor networks(WSN), wireless meshnetwork(WMN).

2 Literature Survey:

Data Aggregation in Wireless Sensor NetworkSensor networks are mainly the collection of the sensor nodes

which are used to send the sensed data to base station. Here,the main aim of the data aggregation algorithms is to collect andaggregate data with considering an efficiency of energy because ofthis network lifetime is enhanced. For processing the less poweras compared to transmitting data, wireless sensor nodes are using

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the sensor nodes. In this paper, data aggregation framework isused on wireless sensor networks. This framework is working asa middleware for aggregation of measured data by the number ofsensor nodes that are connected within the network.

Secure Data Aggregation In Wireless NetworksThis paper is presenting the survey of secure as well as resillent

aggregation protocols for both single and also hierarchical systems.In the hierarchical data aggregation where the hacker achievingwhole control on the sensor nodes within the wireless sensor networkthrough the direct physical access, then security threat is originatedby the node capture attack in this. So that it makes the highrisk of data confidentiality. In this paper, we are finding the nodewhich is attacked by the attacker and the securing that node for thehierarchical data aggregation within the wireless sensor network. Sothat we are resolving the security threat of node capture attacks.

Analysis of Secure Data Aggregation Technique for Wire-less Sensor Network in the Presence of Adversary Environ-ment based on IF algorithm

In this paper, we are using the simple method as Averagingfor aggregation the data from multiple sensor nodes in the wirelesssensor network as considering the limited energy resources and alsocomputational power. In this, iterative filtering algorithms is veryhelpful as per to ascertain the trustworthiness of the data as wellas the reputation of the sensor nodes which is crucial for wirelesssensor network because WSNs are normally unattended , they areact as mostly high vulnerable to node compromising attacks. Sowe use iterative filtering algorithms , which is aggregating the datafrom the multiple sources and also providing the trust estimationfor these sources in wireless sensor network. Usually it is in the formof corresponding weight factors which is assigned to data providedby the each source.

Secure Data Aggregation Technique for Wireless SensorNetworks in the Presence of Collusion Attacks

Iterative filtering algorithm is mainly use for gather and ag-gregating the data from multiple sources in the network and alsoproviding the trust assessment of these sources. In this paper we areusually demonstrate the number of iterative filtering algorithms. Itis significantly more robust and against the collusion attacks whichare formed than the simple averaging methods. To recover this type

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of security issue we are finding an improvement for the iterativefiltering techniques with the help of providing an initial approxi-mation for this type of algorithms. So that it makes them not onlythe collusion robust but also more accurate and fast converging. Sothat for the future Wireless sensor network we consider modifiediterative filtering algorithms which is having the great potential forthe deployment.

3 System Architecture:

Figure 1: System Architecture Using IOT

Mathematical ModuleData Aggregation by Tree Construction

We had considers the network as given network G, Set of allsensor nodes is marked as V which consists the network G and thedistance is r between two nodes (u,v) which is the length betweentwo nodes. G(V, r) is the combination form by these link.

Definitions1. Center of TopologyAny node 0 v is called the center of topology of G (V, r) if 0 v

=min dg (u, v) where dg (u, v) is the hope distance between nodesu and v in G (V, r).

2. Connecting Dominating Set (CDS)Consider any graph G= (V, E), a subset 0 V of V is a dominating

set if each node in V is either in 0 V or adjacent to some node in0 V . Nodes in 0 V are called dominators whereas nodes not in

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0 V are called dominates. A subset C of V is called a connecteddominating set if C is a dominating set and C includes a connectedsub graph.

3. Maximal Independent Set (MIS)The collections of nodes that are separated at two hop distance.

Construct the aggregation tree on the Communication graph G (V,r) using Algorithm1. The basic idea of Algorithm 1 is to constructa tree similar to the breadth-first-search tree, with the followingproperties:

(1) The depth of the tree is within a small constant factor ofthe diameter D (G),

(2) Each internal node will be connected with at most a constantnumber of other internal nodes.

The second property ensures that we can schedule the transmis-sions of internal nodes in constant time-slots. The algorithm basedon the following properties.

(1) We select the topology center of G (V, r) as the root of ourBFS tree. To reduce the latency to a function of n/w radius R(G),instead of network diameter D(G) we had considered the topologycenter selection

(2) After the topology center gathered the aggregated data fromall nodes, it will then send the aggregated result to the sink nodevia the shortest path from the topology centerto 0 v the sink node.

In a tree based network, we had organized sensor nodes intoa tree where data aggregation is performed at intermediate nodesalong the tree and a concise representation of the data is trans-mitted to the root node. Tree based data aggregation is suitablefor applications which involve in-network data aggregation. We canconsider radiation level monitoring as a sample example applicationin a nuclear plant where the maximum value provides the most use-ful information for the safety of the plant. One of the main aspectsof tree-based networks is the construction of an energy efficient dataaggregation tree

Algorithms :A New Approach for Finding Maximal Independent Set

(MIS)According to the definition of maximal independent set, it is

the collection of the nodes which are separated at two hop distancefrom a certain node. Let G be a non-cyclic graph and V be the

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set of all nodes. In this approach for finding MIS, I am using twofunction find MIS() and get node() and call these function in themain algorithm.

In function find MIS() first of all pass a center node and thenstoring all the adjacent node of center in the set S and then foreach node of the set S find rank and visit of node and if the rankof node is 2 and node is not center and visit of node is less thanor equal to two then print the node. Repeat the same process foreach node of graph. The function get node() is worked on the setS. This function isReturn the first node of set S and the decreasethe one in the number of nodes in the set S.

Module Specification & ImplementationsWe are having following modules which we are implementing in

our thesis work.Setting up Network Model

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Our first module is setting up the network model. We considera large-scale, homogeneous sensor network consisting of resource-constrained sensor nodes. The sensor nodes are divided into dis-joint clusters, and each cluster has a cluster head which acts asan aggregator. Data are periodically collected and aggregated bythe aggregator. We assume that each data aggregator has enoughcomputational power to run an IF algorithm for data aggregation.

Figure 2: Screenshot of Tree Formation of Network using clusters

Figure 3: Node Details in Tree Structure

Robust Data AggregationIn order to improve the performance of IF algorithms against

the aforementioned attack scenario, we provide a robust initial esti-mation of the trustworthiness of sensor nodes to be used in the firstiteration of the IF algorithm. Most of the traditional statistical es-timation methods for variance involve use of the sample mean. Forthis reason, proposing a robust variance estimation method in the

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case of skewed sample mean is an essential part of our methodol-ogy. We assume that the stochastic components of sensor errors areindependent random variables with a Gaussian distribution; how-ever, our experiments show that our method works quite well forother types of errors without any modification. Moreover, if errordistribution of sensors is either known or estimated, our algorithmscan be adapted to other distributions to achieve an optimal per-formance. Based on such an estimation of the bias and variance ofeach sensor, the bias estimate is subtracted from sensors readingsand in the next phase of the proposed framework, we provide aninitial estimate of the reputation vector calculated using the MLE.

Figure 4: IPAddress of Router and Data send from Node 1 toNode n

Enhanced Iterative FilteringAccording to the proposed attack scenario, the attacker exploits

the vulnerability of the IF algorithms which originates from a wrongassumption about the initial trustworthiness of sensors. Our contri-bution to address this shortcomings is to employ the results of theproposed robust data aggregation technique as the initial reputa-tion for these algorithms. Moreover, the initial weights for all sensornodes can be computed based on the distance of sensors readingsto such an initial reputation. Our experimental results illustratethat this idea not only consolidates the IF algorithms against theproposed attack scenario, but using this initial reputation improvesthe efficiency of the IF algorithms by reducing the number of iter-ations needed to approach a stationary point within the prescribedtolerance

Accuracy with a Collusion AttackIn order to illustrate the robustness of the proposed data aggre-

gation method in the presence of sophisticated attacks, we synthet-ically generate several data sets by injecting the proposed collusion

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attacks. Therefore, we assume that the adversary employs c (c ¡ n)compromised sensor nodes to launch the sophisticated attack sce-nario proposed. The attacker uses the first compromised nodes togenerate outlier readings in order to skew the simple average of allsensor readings. The adversary then falsifies the last sensor read-ings by injecting the values very close to such skewed average. Thiscollusion attack scenario makes the IF algorithm to converge to awrong stationary point. In order to investigate the accuracy of theIF algorithms with this collusion attack scenario, we syntheticallygenerate several data sets with different values for sensors variancesas well as various number of compromised nodes. The results of thisexperiment validate that our sophisticated attack scenario is causedby the discovered vulnerability in the IF algorithms which sharplydiminishes the contributions of benign sensor nodes when one ofthe sensor nodes reports a value very close to the simple average.

4 Conclusion

We had proposed IOT based Attack -Free Secured Tree Based DataAggregation in WSN. Before the data aggregation happens we areconsidering the critical area of earthqueake where we will be col-lecting data from sensor nodes and then through cloud we will bedetecting nodes which probably gives the attack on existing nodes.When node from set wants to send the data, it sends fragments ofthis encrypted data to other nodes in the same set. After receivingdata, node decrypts data, sums up the fragments and sends theencrypted data to the aggregator. It then aggregates and encryptsthe data with the shared secret key of the sink and forwards it tothe sink. In the next round of aggregation, reselection of set ofnodes with new set of authentication keys is done.

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