A Robust Data Aggregation Method Against Collusion Attacks

Abstract

The invention belongs to the field of Wireless Sensor Networks (WSNs for short), and specifically provides a robust data aggregation method against collusion attack, which is used to more effectively overcome the defects existing in WSNs data aggregation. The method of the invention can detect and directly eliminate the fake attack nodes in the collusion attack, and make them dormant; secondly, the reputation value evaluation method is adopted for the real attack nodes to reduce the weight of the unreliable nodes; and then the evaluated sensor node data is iterated. filter processing. The simulation results show that the method proposed in the present invention reduces the root mean square error of the result data relative to the real data, improves the robustness of the data aggregation method, that is, improves the anti-collusion attack capability of WSNs data aggregation, so that it can be effectively applied to in the actual sensor network.

Description

technical field [0001] The invention belongs to the field of wireless sensor networks (Wireless Sensor Networks, WSNs for short), and relates to a technology for realizing security data aggregation of sensor nodes in wireless sensor networks, in particular to a robust data aggregation method against collusion attacks. Background technique [0002] Wireless sensor networks (WSNs) are widely used in space security monitoring, military technology dissemination, ecological environment monitoring, Internet of Things, intelligent transportation systems, disaster warning monitoring, battlefield situational awareness, etc. Civil and military fields have shown broad application prospects. However, WSNs are usually unattended and lack tamper-proof hardware support. At the same time, due to the openness of wireless channels, vulnerable sensor nodes are easily out of the control of the network or even captured by attackers, which makes the data in the entire network face serious securit...

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Problems solved by technology

[0004] One is the discriminant function of the improved iterative filtering algorithm, such as the improved iterative filter based on the Pearson correlation coefficient in the literature "Zhou YB, Lei T, Zhou T. A robust king algorithm to spamming [J]. EPL, 2011, 94 (4)". The discriminant function of the filtering algorithm; this method can prevent the iterative algorithm from quickly converging to the slope value to a certain extent, but it cannot eliminate the influence of the outliers generated by malicious nodes on the algorithm;

[0005] The other is to evaluate the reliability of sensor nodes before the iterative algorithm, such as the literature "Rezvani M, Ignjatovic A, Bertino E, Jha S.Secure Data Aggregation Technique for WirelessSensor Networks in the Presence of Collusion Attacks[J].IEEE TRANSACTIONS ONDEPENDABLE AND SECUR E COMPUTING, 2015,12(1)” proposes an improved iterative method, which performs maximum likelihood estimation on the data before iteration, and uses the estimated reputation vector as the initial iteration vector, but with malicious nodes The number increases, and the method is inevitably affected by outliers in the random noise scene; another example is the literature "Firoozi F, Zadorozhny VI, Li FY. Subjective Logic-Based In-Network Data Processing for Trust Management in Collocated and Distributed Wireless Sensor Networks[J].IEEE SENSORS JOURNAL,2018,18(15)" is based on the subjective logic mechanism, and according to the proposed reputation threshold, the sliding window method is used to eliminate the nodes with large outliers in the sensors, but the remaining sensors are not data reliability evaluation

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