Big data technology for computer intrusion detection

Abstract

Abstract In order to improve the ability of computer network intrusion detection, the big data technology for computer intrusion detection was studied. This research uses big data technology to build a network intrusion detection model, using clustering algorithms, classification algorithms, and association rule algorithms in data mining to automatically identify the attack patterns in the network and quickly learn and extract the characteristics of network attacks. The experimental results show that the recognition effect of the classification algorithm is obviously better than that of the clustering algorithm and the association rule. With the increase in the proportion of abnormal commands, the accuracy rate can still be maintained at 90%. As a compromise between the classification algorithm and the clustering algorithm, the accuracy rate of the association rule algorithm is basically maintained at more than 75%. It is proved that the big data technology oriented to computer intrusion detection can effectively improve the detection ability of computer network intrusion.