Computer Science ›› 2020, Vol. 47 ›› Issue (2): 287-293.doi: 10.11896/jsjkx.190100047

• Information Security • Previous Articles     Next Articles

DoS Anomaly Detection Based on Isolation Forest Algorithm Under Edge Computing Framework

CHEN Jia,OUYANG Jin-yuan,FENG An-qi,WU Yuan,QIAN Li-ping   

  1. (College of Information Engineering,Zhejiang University of Technology,Hangzhou 310023,China)
  • Received:2019-01-07 Online:2020-02-15 Published:2020-03-18
  • About author:CHEN Jia,born in 1995,postgraduate.His main research interests include internet of things and network resource scheduling;QIAN Li-ping,born in 1981,Ph.D,professor,Ph.D supervisor,is member of China Computer Federation.Her main research interests include wireless communication,deep space communication,cognitive radio network and smart grid.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61572440) and Natural Science Foundation of Zhejiang Province, China (LR16F010003, LR17F010002).

Abstract: With the rapid development of network technology,network attacks have brought huge negative impacts,so network security issues need to be resolved urgently.Aiming at denial of service (DoS) attacks in networks,an anomaly detection method for isolated forest based on edge computing framework was proposed.According to the characteristics of each edge node,the method realizes the reasonable distribution of the model training tasks and effectively improves the utilization efficiency of edge nodes.Meanwhile,the characteristics of edge computing are utilized to realize the offloading of model training tasks from cloud center,so as to better reduce the time consumption of the system and reduce the burdenof the cloud center.In order to verify the effectiveness of the proposed method,the 10%-KDDCUP99 network dataset is preprocessed and partial data used for experiments.Experimental results show that compared with the Support Vector Machine (SVM) and Multi-Layer Perceptron (MLP) methods,time consumption of proposed method is reduced by 90% and 60% respectively,and area under curve (AUC) can reach more than 0.9,which indicates that the method can effectively reduce the system time consumption and ensure a high detection performance.

Key words: Anomaly detection, Data preprocessing, DoS attack, Edge computing, Isolation forest

CLC Number: 

  • TP309.2
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