@inproceedings{77cd967c0fe74b2a9085839cc0a3dbe9,
title = "An Improved Prediction Model for the Network Security Situation",
abstract = "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.",
keywords = "Acceleration, Network security situation, Prediction, SVM",
author = "Jingjing Hu and Dongyan Ma and Liu Chen and Huaizhi Yan and Changzhen Hu",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 4th International Conference on Smart Computing and Communications, SmartCom 2019 ; Conference date: 11-10-2019 Through 13-10-2019",
year = "2019",
doi = "10.1007/978-3-030-34139-8\_3",
language = "English",
isbn = "9783030341381",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer",
pages = "22--33",
editor = "Meikang Qiu",
booktitle = "Smart Computing and Communication - 4th International Conference, SmartCom 2019, Proceedings",
address = "Germany",
}