Analysis of Network Security Countermeasures From the Perspective of Improved FS Algorithm and ICT Convergence


  • Zhihong Zhang Anhui Technical College of Water Resources and Hydroelectric Power, HeFei 231603, China



FS algorithm, extreme learning machine, network intrusion, communications technology


In this paper, the forward selection (FS) algorithm is introduced on the basis of information and communication technology, and the design of intrusion detection method for communication network is carried out. By studying the classification and detection pattern matching of communication network intrusion behavior, extracting the intrusion behavior features of communication network based on FS algorithm, and optimizing the intrusion detection and learning effect based on the limit learning machine, the intrusion behavior attributes of communication network are clarified, and a new detection method is proposed to solve the problems of low detection accuracy and low recall in the current intrusion behavior detection of complex communication network environments. Compared with the intrusion detection method based on GA-SVM algorithm, the accuracy of the detection results reaches 94.23%, and the recall rate exceeds 97%, which is obviously better than the 85% accuracy and 75% recall rate of the traditional detection method, which can ensure the security of the communication network environment. In addition, this paper proposes the APDR dynamic comprehensive information security assurance system model, which has considerable flexibility and can respond to current network security requirements.


Download data is not yet available.

Author Biography

Zhihong Zhang, Anhui Technical College of Water Resources and Hydroelectric Power, HeFei 231603, China

Zhihong Zhang is a mathematics student of Anhui University since1994. He graduated from Anhui University with a bachelor’s degree in Applied Mathematics in 1998, and then obtained a master’s degree in Computer Science and Technology from Anhui University in 2004. He is mainly engaged in the research of neural network algorithm. Since his graduation, he has been working in the Anhui Technical College Of Water Resources And Hydroelectric Power, engaged in teaching and scientific research, and his research field is the neural network security in the direction of the Internet of Things.


Azeez, N. A., S. O. Idiakose, C. J. Onyema, and Van Der Vyver, C. 2021. Cyberbullying Detection in Social Networks: Artificial Intelligence Approach. Journal of Cyber Security and Mobility, 745–774.

Alqarni, A. A. 2022. Majority Vote-Based Ensemble Approach for Distributed Denial of Service Attack Detection in Cloud Computing. Journal of Cyber Security and Mobility. 265–278.

Fujs, D., Mihelic, A., and S. Vrhovec. 2019. Social Network Self-Protection Model: What Motivates Users to Self-Protect? Journal of Cyber Security and Mobility. 467–492.

Li, X., H. Li, B. Sun, and F. Wang. 2018. Assessing information security risk for an evolving smart city based on fuzzy and grey FMEA. Journal of Intelligent and Fuzzy Systems. 34(4): 2491–2501.

Yan, N. 2022. Legal Guarantee of Smart City Pilot and Green and Low-Carbon Development. Journal of Environmental and Public Health. doi: 10.1155/2022/4280441

Zhang, C. 2020. Design and application of fog computing and Internet of Things service platform for a smart city. Future Generation Computer Systems. 112: 630–640.

Garetti, M., and M. Taisch. 2012. Sustainable manufacturing: trends and research challenges. Production planning and control. 23(2–3): 83–104.

De Reuver, M., C. Sørensen, and R. C. Basole. 2018. The digital platform: a research agenda. Journal of information technology. 33(2): 124–135.

Senge, P. M., G. Carstedt, and P. L. Porter. 2001. Next industrial revolution. MIT Sloan management review, 42(2): 24–38.

Ijaz, S., M. A. Shah, A. Khan, and M. Ahmed. 2016. Smart cities: A survey on security concerns. International Journal of Advanced Computer Science and Applications, 7(2).

Yampolskiy, R. V. 2022. On the Controllability of Artificial Intelligence: An Analysis of Limitations. Journal of Cyber Security and Mobility. 321–404.

Zhang, L., X. Hu, W. Rasheed, T. Huang, and C. Zhao. 2019. An enhanced steganographic code and its application in voice-over-IP steganography. IEEE Access, 7, 97187–97195.

Aceto, G., V. Persico, and A. Pescapé. 2019. A survey on information and communication technologies for industry 4.0: State-of-the-art, taxonomies, perspectives, and challenges. IEEE Communications Surveys and Tutorials. 21(4): 3467–3501.

Bodapati, J. D., and N. Veeranjaneyulu. 2019. Feature extraction and classification using deep convolutional neural networks. Journal of Cyber Security and Mobility. 261–276.

Lin, L. W. 2010. Corporate social responsibility in China: Window dressing or structural change. Berkeley J. Int’l L. 28, 64.

Wang, W., and Z. Lu. 2013. Cyber security in the smart grid: Survey and challenges. Computer networks. 57(5), 1344–1371.

Siponen, M. T. 2000. A conceptual foundation for organizational information security awareness. Information management and computer security.

Bulgurcu, B., Cavusoglu, H., and Benbasat, I. 2010. Information security policy compliance: an empirical study of rationality-based beliefs and information security awareness. MIS quarterly, 523–548.

Kabiri, P., and A. A. Ghorbani. 2005. Research on intrusion detection and response: A survey. Int. J. Netw. Secur. 1(2): 84–102.

Yamaguchi, Y., A. Ogawa, A. Takeda, and S. Iwata. 2015. Cyber security analysis of power networks by hypergraph cut algorithms. IEEE Transactions on Smart Grid. 6(5): 2189–2199.




How to Cite

Zhang Z. Analysis of Network Security Countermeasures From the Perspective of Improved FS Algorithm and ICT Convergence. JCSANDM [Internet]. 2023 Mar. 7 [cited 2023 Dec. 4];12(01):1–24. Available from:



Cyber Security Issues and Solutions