Research On Sybil Attacks Detection And Secure Data Aggregation For Wireless Sensor Networks

With the rapid development of computer technology,embedded technology and wireless communication technology,wireless sensor network(WSN)is being widely used in many applications,such as environmental monitoring,auxiliary medical care,military defense,target tracking and industrial and agricultural fields.To be different from traditional networks,wireless sensor network is formed by a large number of sensor nodes selforganized,sensor nodes can real-time monitoring,sensing and acquisition of sensitive information,and through the collaboration between the nodes,send the data to the user in the form of multiple hops.Wireless sensor network has many advantages such as simple and fast deployment,without human management,data automatic acquisition,etc.As the application scenarios of wireless sensor network become more and more abundant,different security challenges have been raised in wireless sensor network.Wireless sensor network nodes are mostly cheap nodes and are deployed in the field or the hostile area.Due to the limitation of sensor nodes resources and the threat of the network environment,sensor nodes are easy to be captured by attackers and transformed into malicious nodes,and malious nodes can be redeployed into the network to launch internal attacks,which have great harm to the network.Sybil attack,as a typical form of attack,claims to have multiple identities to participate in network activities,harms various operating mechanisms in the network,and seriously threatens the security of the network.At the same time,wireless sensor network is a data-centric network,the importance of data is self-evident.For resource constraints of sensor nodes,data aggregation operation is the key to remove redundant data,to reduce the amount of data transmission and prolong the network lifetime,the aggregation results contain more information,if the malicious nodes forge and send false data to change the aggregation results,will lead to users to make wrong decision,so it is necessary to study secure data aggregation mechanism to ensure the correctness of the data aggregation.1)In the wireless sensor network,sybil attack nodes launch attacks by forging multiple identities.If different sybil identities send messages with different transmitting powers,which makes the sybil attack behavior difficult to detect.To solve this problem,this paper proposes a sybil attack detection scheme based on the angle of arrival(AOA)in heterogeneous network environment.In this scheme,heterogeneous nodes detect the angle of arrival from surrounding nodes,and use the angle information to establish a list of suspicious sybil nodes.Through the information interaction between neighboring heterogeneous nodes,the sybil attack node can be located cooperatively.For the special case of heterogeneous node sparse,a single heterogeneous node enhanced detection mechanism is proposed to detect sybil node.Through theoretical analysis and simulation experiments,the scheme can quickly and accurately identify the malicious nodes,reduce the energy consumption of the nodes,and extend the network life.2)Malicious nodes in a wireless sensor network,if any,will cause a great deviation in aggregation data.Besides,such malicious nodes assigned with the aggregation task can forge and send a mass of false data,as a result of which,the network may consume a mass of resources and users may make wrong decisions.Concerning this problem,a security data aggregation scheme based on node adaptive monitoring mode is proposed in this paper.According to the scheme,a master cluster head node and vice cluster node are selected,wherein,the vice cluster head node verifies the aggregation data of the master cluster head,the intra-cluster monitoring nodes monitor the behavior of the master and vice cluster head nodes in an adaptive manner;Besides,the confidence interval theory is introduced to evaluation and calculation of the credibility of nodes and highly credible node data are selected for data aggregation.Both theory analysis and simulation experience show that this scheme can not only effectively recognize and shield malicious nodes and enhance accuracy of data aggregation but also prevent malicious master cluster head nodes from forging and sending a mass of false data.Figure [23] table [7] reference [71]...