| Title | APT Attack Situation Assessment Model Based on optimized BP Neural Network |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Fu, Tian, Lu, Yiqin, Zhen, Wang |
| Conference Name | 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) |
| Keywords | adaptive genetic algorithm, advanced persistent threat, APT, Attack Path, attack situation assessment model, Backpropagation, BP Neural Network, Collaboration, genetic algorithms, Metrics, Network security, neural nets, Neural Network, Neural Network Security, Neural networks, policy-based governance, Predictive models, pubcrawl, security, security of data, security situation, Situation Prediction, Timing, Training, Training samples, Trojan horses |
| Abstract | In this paper, it first analyzed the characteristics of Advanced Persistent Threat (APT). according to APT attack, this paper established an BP neural network optimized by improved adaptive genetic algorithm to predict the security risk of nodes in the network. and calculated the path of APT attacks with the maximum possible attack. Finally, experiments verify the effectiveness and correctness of the algorithm by simulating attacks. Experiments show that this model can effectively evaluate the security situation in the network, For the defenders to adopt effective measures defend against APT attacks, thus improving the security of the network. |
| DOI | 10.1109/ITNEC.2019.8729178 |
| Citation Key | fu_apt_2019 |
APT Attack Situation Assessment Model Based on optimized BP Neural Network