Abstract: Aiming at the problems of low detection accuracy and poor blocking effect of attack traffic in wireless sensor networks, a wireless sensor network attack traffic blocking model based on random forest algorithm is constructed. Through TF-IDF algorithm, the feature of payload is automatically extracted based on the word frequency matrix of characters (words). According to the characteristic results, the random forest algorithm is used to classify the network traffic through the word frequency matrix, and the traffic attack in the network can be traced based on the classification results to complete the detection of abnormal wireless sensor networks. The packet filtering of the flow table is used to block the traffic of wireless sensor attack. Experiments show that, when detecting attack traffic, the detection accuracy of the model can reach 100%, the highest harmonic mean is 99.18%, and the highest error rate is only 7.3%, and the false positive rate is only 5.5%. At the same time, it can effectively block the network attack traffic and restore the network to normal in a short time. It has good attack traffic detection effect and attack traffic blocking effect.

Key words: random forest algorithm, wireless sensor network, attack flow, blocking model