Anti-collusion attack robust data aggregation method

Abstract

The invention belongs to the field of wireless sensor networks (WSNs for short), and particularly provides an anti-collusion attack robust data aggregation method, which is used for more effectively overcoming the defects in WSNs data aggregation. According to the method, pseudo attack nodes in collusion attacks can be detected and directly eliminated, so that the pseudo attack nodes are dormant; secondly, a reputation value evaluation method is adopted for a real attack node, and the weight of an unreliable node is reduced; and then iterative filtering processing is carried out on the evaluated sensor node data. Simulation results show that the method provided by the invention reduces the root-mean-square error of result data relative to real data, improves the robustness of a data aggregation method, i.e., improves the collusion attack resistance of WSNs data aggregation, and enables the WSNs data aggregation method to be effectively applied to an actual sensor network.

Description

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

AI Technical Summary

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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