Neighborhood Rough Residual Network–Based Outlier Detection Method in IoT-Enabled Maritime Transportation Systems
Qiong Chen, Liangru Xie, Lirong Zeng, Sining Jiang, Weiping Ding, Xiaomeng Huang, Hao Wang
Abstract
Outlier detection can identify anomalies in large-scale data. To provide reliability and security for Internet of Things (IoT)-enabled maritime transportation systems (MTSs), in this paper we propose an outlier detection method based on the neighborhood rough residual network (NRRN). We calculate the neighborhood approximation accuracy and neighborhood conditional entropy to obtain the neighborhood combined entropy describing the discrimination ability of the condition attribute subset to the information system. We then delete the redundant attributes according to the attribute combination importance derived from the neighborhood combined entropy. The data after attribute reduction are used to train the convolutional neural network, and the residual network (ResNet50) is used to avoid the degradation of model performance caused by the increase in the number of network layers. The proposed method is compared with mainstream outlier detection algorithms on a fishing vessel operation dataset. Experiments show that the proposed method can greatly improve the accuracy of outlier detection while taking into account interpretability and computational efficiency, thereby ensuring the data integrity of IoT-enabled MTSs.