TY - GEN
T1 - An Improved Prediction Model for the Network Security Situation
AU - Hu, Jingjing
AU - Ma, Dongyan
AU - Chen, Liu
AU - Yan, Huaizhi
AU - Hu, Changzhen
N1 - Publisher Copyright:
© 2019, Springer Nature Switzerland AG.
PY - 2019
Y1 - 2019
N2 - This research seeks to improve the long training time of traditional methods that use support vector machine (SVM) for cyber security situation prediction. This paper proposes a cyber security situation prediction model based on the MapReduce and SVM. The base classifier for this model uses an SVM. In order to find the optimal parameters of the SVM, parameter optimization is performed by the Cuckoo Search (CS). Considering the problem of time cost when a data set is too large, we choose to use MapReduce to perform distributed training on SVMs to improve training speed. Experimental results show that the SVM network security situation prediction model using MapReduce and CS has improved the accuracy and decreased the training time cost compared to the traditional SVM prediction model.
AB - This research seeks to improve the long training time of traditional methods that use support vector machine (SVM) for cyber security situation prediction. This paper proposes a cyber security situation prediction model based on the MapReduce and SVM. The base classifier for this model uses an SVM. In order to find the optimal parameters of the SVM, parameter optimization is performed by the Cuckoo Search (CS). Considering the problem of time cost when a data set is too large, we choose to use MapReduce to perform distributed training on SVMs to improve training speed. Experimental results show that the SVM network security situation prediction model using MapReduce and CS has improved the accuracy and decreased the training time cost compared to the traditional SVM prediction model.
KW - Acceleration
KW - Network security situation
KW - Prediction
KW - SVM
UR - http://www.scopus.com/inward/record.url?scp=85076176645&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-34139-8_3
DO - 10.1007/978-3-030-34139-8_3
M3 - Conference contribution
AN - SCOPUS:85076176645
SN - 9783030341381
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 22
EP - 33
BT - Smart Computing and Communication - 4th International Conference, SmartCom 2019, Proceedings
A2 - Qiu, Meikang
PB - Springer
T2 - 4th International Conference on Smart Computing and Communications, SmartCom 2019
Y2 - 11 October 2019 through 13 October 2019
ER -