A wireless sensor abnormal data detection method based on unsupervised learning

Abstract

The invention discloses a wireless sensor abnormal data detection method based on unsupervised learning. The method comprises the following steps of 1, acquiring m pieces of data continuously acquiredby a wireless sensor node to form a training sample set; 2, establishing a quarter hypersphere support vector machine model of which the sphere center is located at the origin of high-dimensional space coordinates, wherein the minimum support radius is R; 3, optimizing the parameters of the 1/4 hypersphere support vector machine model by applying a particle swarm algorithm and a training sample set to obtain an optimized model; 4, obtaining m + 1 pieces of data Tq continuously collected by the wireless sensor nodes, calculating the distance d (Tm + 1) of Tm + 1 in the mapping space of the optimization model to the sphere center, and if d (Tm + 1) is smaller than or equal to R, determining that Tm + 1 is normal data; if d (Tm + 1) is greater than R, using a set {T1, T2,... Tm} as a training sample set to retrain the model, and calculating the minimum support radius Rnew, and calculating the distance d (Tm + 1) new from the Tm + 1 in the updated mapping space of the model to the spherecenter, and if d (Tm + 1) new < = Rnew, determining that the data Tm + 1 is normal data, otherwise, determining that the Tm + 1 is abnormal data. According to the method, the unsupervised learning isadopted, and the samples do not need to be labeled, so that the detection accuracy is higher.

Description

technical field [0001] The invention belongs to the field of wireless sensor security, in particular to a method for detecting abnormal data of wireless sensors based on unsupervised learning. Background technique [0002] In recent years, wireless sensor networks have attracted more and more attention from all over the world, and have become an extremely important research field. While integrating the fields of communication and sensing, the achieved results have also laid the foundation for research in other fields. . Wireless sensor networks have a wide range of usage scenarios, such as environmental monitoring applications, military reconnaissance, medical equipment, smart agriculture, oil refining industry, etc. [0003] A wireless sensor network is usually composed of multiple sensors densely deployed in a wide range. These small sensors are used to monitor and collect surrounding environmental information, and send this information to the sink node through radio comm...

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Problems solved by technology

Many related methods have also been proposed, such as the BOD (Quarter Sphere Based Distributed Anomaly Detection in Wireless Sensor Networks) method proposed by S. Rajasegarar et al., and the AOD (Ensuring high sensordata quality through use of online outlier detection techniques) method proposed by Yang Zhang et al. It is realized on the basis of a quarter hypersphere support vector machine, but there is still room for improvement in the detection rate and false alarm rate of these methods

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