852
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Network security situation assessment based on dual attention mechanism and HHO-ResNeXt

, &
Article: 2174080 | Received 24 Nov 2022, Accepted 25 Jan 2023, Published online: 20 Feb 2023

References

  • Akwetey, H. M., & Danquah, P. (2022). Predicting cyber-attack using cyber situational awareness: The case of independent power producers (IPPs). International Journal of Advanced Computer Science and Applications, 13(1), 700–709. https://doi.org/10.48550/arXiv.2202.01778
  • Bass, T. (2000). Intrusion detection systems and multisensor data fusion. Communications of the ACM, 43(4), 99–105. https://doi.org/10.1145/332051.332079
  • Bass, T., & Gruber, D. (1999). A glimpse into the future of id. The Magazine of USENIX & SAGE, 24(3), 40–49.
  • Cao, Y., Zhu, G., Qi, X., & Zou, J. (2021). 基于随机森林的入侵检测分类研究 [Research on intrusion detection classification based on random forest]. Computer Science, 48(S1), 459–463. https://doi.org/10.11896/jsjkx.200600161
  • Endsley, M. R. (1988). Design and evaluation for situation awareness enhancement. In Proceedings of the Human Factors Society Annual Meeting (Vol. 32, No. 2, pp. 97–101). Sage Publications.
  • Gong, J., Zang, X., Su, Q., Hu, X., & Xu, J. (2017). 网络安全态势感知综述 [Survey of network security situation awareness]. Journal of Software, 28((04|4)), 1010–1026. https://doi.org/10.13328/j.cnki.jos.005142
  • He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 770–778). IEEE Computer Society.
  • Heidari, A. A., Mirjalili, S., Faris, H., Aljarah, I., Mafarja, M., & Chen, H. (2019). Harris hawks optimization: Algorithm and applications. Future Generation Computer Systems, 97, 849–872. https://doi.org/10.1016/j.future.2019.02.028
  • Hu, J., Shen, L., & Sun, G. (2018). Squeeze-and-excitation networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 7132–7141). IEEE Computer Society.
  • Jiang, L., Jayatilaka, A., Nasim, M., Grobler, M., Zahedi, M., & Babar, M. A. (2022). Systematic literature review on cyber situational awareness visualizations. IEEE Access, 10, 57525–57554. https://doi.org/10.1109/ACCESS.2022.3178195
  • Ke, G., Chen, R. S., Chen, Y. C., & Yeh, J. H. (2021). Network security situation prediction method based on support vector machine optimized by artificial Bee colony algorithms. Journal of Computers, 32(1), 144–153. https://doi.org/10.3966/199115992021023201012
  • Li, Y., Yao, T., Pan, Y., & Mei, T. (2022). Contextual transformer networks for visual recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2), 1489–1500. https://doi.org/10.1109/TPAMI.2022.3164083
  • Moustafa, N., & Slay, J. (2015, November). UNSW-NB15: A comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set). In 2015 Military Communications and Information Systems Conference (MilCIS) (pp. 1–6). Canberra, ACT, Australia: IEEE.
  • Nazir, H. M. J., & Han, W. (2022). Proliferation of cyber situational awareness: Today’s truly pervasive drive of cybersecurity. Security and Communication Networks, 2022, 1–16. https://doi.org/10.1155/2022/6015253
  • Revathi, S., & Malathi, A. (2013). A detailed analysis on NSL-KDD dataset using various machine learning techniques for intrusion detection. International Journal of Engineering Research & Technology (IJERT), 2(12), 1848–1853.
  • Samuel, O. S. (2021). Cyber situation awareness perception model for computer network. International Journal of Advanced Computer Science and Applications, 12(1), https://doi.org/10.14569/IJACSA.2021.0120147
  • Tavallaee, M., Bagheri, E., Lu, W., & Ghorbani, A. A. (2009, July). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6). IEEE.
  • Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., & Hu, Q. (2020, June). ECA-Net: Efficient channel attention for deep convolutional neural networks. In Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13–19). IEEE.
  • Wu, W., & Yang, C. (2022). An overview on network security situation awareness in internet. International Journal of Network Security, 24(3), 450–456. https://doi.org/10.6633/IJNS.202205_24(3).08
  • Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 1492–1500). IEEE.
  • Yang, H., Zeng, R., Xu, G., & Zhang, L. (2021). A network security situation assessment method based on adversarial deep learning. Applied Soft Computing, 102, 107096. https://doi.org/10.1016/j.asoc.2021.107096
  • Yang, H., Zhang, Z., Xie, L., & Zhang, L. (2022). Network security situation assessment with network attack behavior classification. International Journal of Intelligent Systems, 37(10), 6909–6927. https://doi.org/10.1002/int.22867
  • Yuan, L. (2021). Prediction of network security situation awareness based on an improved model combined with neural network. Security and Privacy, 4(6), e181. https://doi.org/10.1002/spy2.181
  • Zhang, R. C., Zhang, Y. C., Liu, J., & Fan, Y. D. (2019). Network security situation prediction method using improved convolution neural network. Computer Engineering and Applications, 55((06|6)), 86–93. https://doi.org/10.3778/j.issn.1002-8331.1808-0016
  • Zhang, S., Xie, X., & Xu, Y. (2019). Intrusion detection method based on a deep convolutional neural network. Journal of Tsinghua University (Science and Technology), 59(1), 44–52. https://doi.org/10.16511/j.cnki.qhdxxb.2019.22.004
  • Zhao, D., Ji, G., & Zeng, S. (2022). A network security situation assessment method based on multi-attention mechanism and HHO-ResNeXt. In Xiaofeng Chen (Ed.), International Symposium on Security and Privacy in Social Networks and Big Data (pp. 199–211). Springer.
  • Zheng, W. (2020). Research on situation awareness of network security assessment based on Dempster-Shafer. In MATEC Web of Conferences (Vol. 309, p. 02004). EDP Sciences.