| Title | Cyber Security Situational Awareness Jointly Utilizing Ball K-Means and RBF Neural Networks |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Peng, Cheng, Yongli, Wang, Boyi, Yao, Yuanyuan, Huang, Jiazhong, Lu, Qiao, Peng |
| Conference Name | 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP) |
| Date Published | dec |
| Keywords | Ball k-means clustering, Clustering algorithms, Communication networks, composability, Cyber-physical systems, Data models, Metrics, Neural Network Security, Neural networks, policy-based governance, Prediction algorithms, Predictive Metrics, Predictive models, pubcrawl, RBF neural network, Resiliency, security, situation awareness, situational awareness |
| Abstract | Low accuracy and slow speed of predictions for cyber security situational awareness. This paper proposes a network security situational awareness model based on accelerated accurate k-means radial basis function (RBF) neural network, the model uses the ball k-means clustering algorithm to cluster the input samples, to get the nodes of the hidden layer of the RBF neural network, speeding up the selection of the initial center point of the RBF neural network, and optimize the parameters of the RBF neural network structure. Finally, use the training data set to train the neural network, using the test data set to test the accuracy of this neural network structure, the results show that this method has a greater improvement in training speed and accuracy than other neural networks. |
| DOI | 10.1109/ICCWAMTIP51612.2020.9317377 |
| Citation Key | peng_cyber_2020 |
Cyber Security Situational Awareness Jointly Utilizing Ball K-Means and RBF Neural Networks