With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the develo...With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the development of fog computing.Fog computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more efficiently.The main function of fog computing is to reduce the burden of cloud servers.By placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and calculation.Many of the information collected by IoT devices are malicious traffic,which contains a large number of malicious attacks.Because IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog node.In response to this situation,we propose an intrusion detection system based on distributed ensemble design.The system mainly uses Convolutional Neural Network(CNN)as the first-level learner.In the second level,the random forest will finally classify the prediction results obtained in the first level.This paper uses the UNSW-NB15 dataset to evaluate the performance of the model.Experimental results show that the model has good detection performance for most attacks.展开更多
基金supported in part by the Beijing Natural Science Foundation (No.4212015)Natural Science Foundation of China (No.61801008)+2 种基金China Ministry of Education China Mobile Scientific Research Foundation (No.MCM20200102)China Postdoctoral Science Foundation (No.2020M670074)Beijing Municipal Commission of Education Foundation (No.KM201910005025).
文摘With the rapid development of the Internet of Things(IoT),all kinds of data are increasing exponentially.Data storage and computing on cloud servers are increasingly restricted by hardware.This has prompted the development of fog computing.Fog computing is to place the calculation and storage of data at the edge of the network,so that the entire Internet of Things system can run more efficiently.The main function of fog computing is to reduce the burden of cloud servers.By placing fog nodes in the IoT network,the data in the IoT devices can be transferred to the fog nodes for storage and calculation.Many of the information collected by IoT devices are malicious traffic,which contains a large number of malicious attacks.Because IoT devices do not have strong computing power and the ability to detect malicious traffic,we need to deploy a system to detect malicious attacks on the fog node.In response to this situation,we propose an intrusion detection system based on distributed ensemble design.The system mainly uses Convolutional Neural Network(CNN)as the first-level learner.In the second level,the random forest will finally classify the prediction results obtained in the first level.This paper uses the UNSW-NB15 dataset to evaluate the performance of the model.Experimental results show that the model has good detection performance for most attacks.
