关键词: 物联网, DNN神经网络, 异常攻击检测, 决策树, 特征筛选

Abstract: In order to solve the problem that the accuracy of traditional Abnormal attack detection model of Internet of Things is insufficient when training samples are insufficient, decision tree (DT) is introduced to improve the deep neural network (DNN) model. The addition of decision tree makes it easier to obtain the optimal salient feature set when DNN conducts and features extraction. At the same time, features can be summarized through decision tree, and new features can be inferred through DNN's perceptual learning ability, enabling the model to identify more Internet of Things behaviors. At last, the improved model is validated by computer simulation performance, and the results show that the DT - within DNN model can attack of black holes, DDoS attacks and other Internet attack testing, and testing has relatively higher precision and recall rate and F1 score, proved that the detection model can be applied to a variety of Internet of things attack type testing, has a certain practicality.

Key words: Internet of Things, DNN neural network, abnormal attack detection, decision tree, feature screening