Based on Memory of Key Information Infrastructure Security Detection Technology

被引:0
|
作者
Li J. [1 ]
Cui J. [1 ]
Shi L. [1 ]
机构
[1] China Information Technology Security Evaluation Center, Beijing
关键词
Attack behavior determination; Attack chain; Bidirectional analysis; Web application attack cycle;
D O I
10.15918/j.tbit1001-0645.2019.09.017
中图分类号
学科分类号
摘要
A key information infrastructure security detection technology was proposed based on "memory" to overcome the limitations of the traditional IDS (intrusion detection technology) and WAF (web application firewall) technology in Web attack detection, in this paper. Analyzing comprehensively the three processes of the Web application attack cycle, an attack chain technology based method was used to be able to analyze the real-time data and historical data of Web data bidirectionally, detect various fragmented and persistent attack means, and simultaneously perceive all kinds of vulnerabilities when hackers use attacks, so as to understand and grasp the status of network risk in real time. © 2019, Editorial Department of Transaction of Beijing Institute of Technology. All right reserved.
引用
收藏
页码:987 / 990
页数:3
相关论文
共 8 条
  • [1] Kermany D., Goldbaum M., Valentim C., Et al., Identifying medical diagnoses and treatable diseases by image-based deep learning, Cell, 172, 5, pp. 1122-1131, (2018)
  • [2] Carlucci F., Porzi L., Caputo B., Et al., Autodial: automatic domain alignment layers, International Conference on Computer Vision, (2017)
  • [3] Trust W., Trustwava global security report
  • [4] Bousmalis K., Trigeorgis G., Silberman N., Et al., Domain separation networks, Advances in Neural Information Processing Systems, 29, pp. 343-351, (2016)
  • [5] Long M., Wang J., Cao Y., Et al., Deep learning of transferable representation for scalable domain adaptation, IEEE Transactions on Knowledge and Data Engineering, 28, 8, pp. 2027-2040
  • [6] Ganin Y., Ustinova E., Ajakan H., Et al., Domain-adversarial training of neural networks, Journal of Machine Learning Research, 17, 59, pp. 1-35, (2016)
  • [7] Luo S., Zhang C., Zhou M., Et al., Research on penetration test attack model based on time Petri net, Transactions of Beijing Institute of Technology, 35, 1, pp. 92-96, (2015)
  • [8] Li L., Yu Y., Bai S., Et al., Intrusion detection model based on double training technique, Transactions of Beijing Institute of Technology, 37, 12, pp. 1246-1252, (2017)