Research on the Quality Assessment and Protection of Network Security Body Based on Intelligent Induction and Deep Learning
DOI:
https://doi.org/10.13052/jcsm2245-1439.1363Keywords:
Intelligent sensing, Block chain, Access control, Data storage, Network securityAbstract
This mechanism mainly saves the induction information in the distributed cloud storage node, and saves the message summary of the induction information in the block chain node, and then the corresponding relationship between the cloud storage node and the block chain node is saved in the induction information management machine. When the user reads the data, the identity authentication is first completed at the induction information management machine, and the induction information is obtained through the key, and finally the data verification is completed at the block chain node. The main advantages of this mechanism are: using block chain to store intelligent sensing information, and using chain storage to reduce the cost of storage, thus enhancing the scalability of block chain storage. This mechanism uses new hash chains to transmit inductive information, thus improving the security of transmission. Through this study proposes an access control strategy for intelligent sensing information. In this way, the user with the key can quickly complete the work certificate and complete the access, while the illegal intruder who does not hold the key cannot calculate the work certificate of the next block based on the existing block, so he cannot access the intelligence. Under the network topology set in this paper, the model with step 2 is significantly better than step 1 and 3, with 18.30% and 75.01% reduction on MAPE and 15.66% and 87.79% reduction on RMSE. Through hidden Markov, the security situation of the information system under the time series is determined. Through SSIPN, the security event is not used as a single situation assessment index, but the network topology and node vulnerability are included in the assessment scope to enhance the correlation between the security event and each node in the information system. Based on SSIPN, the weight allocation algorithm of the corresponding nodes is proposed, which accurately reflects the impact of the level of the nodes on the network on the overall situation, and realizes the security situation assessment of the overall network.
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D. Adesina, C. C. Hsieh, Y. E. Sagduyu, and L. J. Qian, “Adversarial Machine Learning in Wireless Communications Using RF Data: A Review,” IEEE Communications Surveys and Tutorials, vol. 25, no. 1, pp. 77–100, 2023.
Y. Afaq and A. Manocha, “Blockchain and Deep Learning Integration for Various Application: A Review,” Journal of Computer Information Systems, vol. 64, no. 1, pp. 92–105, 2024.
G. Agrawal, A. Kaur, and S. Myneni, “A Review of Generative Models in Generating Synthetic Attack Data for Cybersecurity,” Electronics, vol. 13, no. 2, pp. 31, 2024.
H. Ahmetoglu and R. Das, “A comprehensive review on detection of cyber-attacks: Data sets, methods, challenges, and future research directions,” Internet of Things, vol. 20, pp. 25, 2022.
O. A. Alimi, K. Ouahada, and A. M. Abu-Mahfouz, “A Review of Machine Learning Approaches to Power System Security and Stability,” IEEE Access, vol. 8, pp. 113512–113531, 2020.
M. A. Amanullah et al., “Deep learning and big data technologies for IoT security,” Computer Communications, vol. 151, pp. 495–517, 2020.
R. Ameri, C. C. Hsu, and S. S. Band, “A systematic review of deep learning approaches for surface defect detection in industrial applications,” Engineering Applications of Artificial Intelligence, vol. 130, pp. 24, 2024.
L. Aversano, M. L. Bernardi, M. Cimitile, and R. Pecori, “A systematic review on Deep Learning approaches for IoT security,” Computer Science Review, vol. 40, pp. 18, 2021.
K. Barik, S. Misra, K. Konar, L. Fernandez-Sanz, and K. Murat, “Cybersecurity Deep: Approaches, Attacks Dataset, and Comparative Study,” Applied Artificial Intelligence, vol. 36, no. 1, pp. 24, 2022.
S. Bharati and P. Podder, “Machine and Deep Learning for IoT Security and Privacy: Applications, Challenges, and Future Directions,” Security and Communication Networks, vol. 2022, pp. 41, 2022.
E. Btoush, X. J. Zhou, R. Gururajan, K. C. Chan, R. Genrich, and P. Sankaran, “A systematic review of literature on credit card cyber fraud detection using machine and deep learning,” Peerj Computer Science, vol. 9, pp. 66, 2023.
J. Chen, D. D. Wu, and R. Y. Xie, “Artificial intelligence algorithms for cyberspace security applications: a technological and status review,” Frontiers of Information Technology & Electronic Engineering, vol. 24, no. 8, pp. 1117–1142, 2023.
J. R. Cheng, Y. Yang, X. Y. Tang, N. X. Xiong, Y. Zhang, and F. F. Lei, “Generative Adversarial Networks: A Literature Review,” Ksii Transactions on Internet and Information Systems, vol. 14, no. 12, pp. 4625–4647, 2020.
D. Dai and S. Boroomand, “A Review of Artificial Intelligence to Enhance the Security of Big Data Systems: State-of-Art, Methodologies, Applications, and Challenges,” Archives of Computational Methods in Engineering, vol. 29, no. 2, pp. 1291–1309, 2022.
P. Dixit and S. Silakari, “Deep Learning Algorithms for Cybersecurity Applications: A Technological and Status Review,” Computer Science Review, vol. 39, pp. 15, 2021.
J. Du, Q. Wei, Y. S. Wang, and X. J. Sun, “A Review of Deep Learning-Based Binary Code Similarity Analysis,” Electronics, vol. 12, no. 22, pp. 18, 2023.
L. N. Ge, H. A. Li, X. Wang, and Z. Wang, “A review of secure federated learning: Privacy leakage threats, protection technologies, challenges and future directions,” Neurocomputing, vol. 561, pp. 18, 2023.
A. Halbouni, T. S. Gunawan, M. H. Habaebi, M. Halbouni, M. Kartiwi, and R. Ahmad, “Machine Learning and Deep Learning Approaches for CyberSecurity: A Review,” IEEE Access, vol. 10, pp. 19572–19585, 2022.
J. Kaur, U. Garg, and G. Bathla, “Detection of cross-site scripting (XSS) attacks using machine learning techniques: a review,” Artificial Intelligence Review, vol. 56, no. 11, pp. 12725–12769, 2023.
A. R. Khan, M. Kashif, R. H. Jhaveri, R. Raut, T. Saba, and S. A. Bahaj, “Deep Learning for Intrusion Detection and Security of Internet of Things (IoT): Current Analysis, Challenges, and Possible Solutions,” Security and Communication Networks, vol. 2022, pp. 13, 2022.
G. Kornaros, “Hardware-Assisted Machine Learning in Resource-Constrained IoT Environments for Security: Review and Future Prospective,” IEEE Access, vol. 10, pp. 58603–58622, 2022.
C. Kumar, T. S. Bharati, and S. Prakash, “Online Social Network Security: A Comparative Review Using Machine Learning and Deep Learning,” Neural Processing Letters, vol. 53, no. 1, pp. 843–861, 2021.
B. Lampe and W. Z. Meng, “A survey of deep learning-based intrusion detection in automotive applications,” Expert Systems with Applications, vol. 221, pp. 23, 2023.
J. Lansky et al., “Deep Learning-Based Intrusion Detection Systems: A Systematic Review,” IEEE Access, vol. 9, pp. 101574–101599, 2021.
S. W. Lee et al., “Towards secure intrusion detection systems using deep learning techniques: Comprehensive analysis and review,” Journal of Network and Computer Applications, vol. 187, pp. 22, 2021.
F. C. Liu, M. Li, X. X. Liu, T. Xue, J. Ren, and C. Y. Zhang, “A Review of Federated Meta-Learning and Its Application in Cyberspace Security,” Electronics, vol. 12, no. 15, pp. 35, 2023.
A. Miglani and N. Kumar, “Blockchain management and machine learning adaptation for IoT environment in 5G and beyond networks: A systematic review,” Computer Communications, vol. 178, pp. 37–63, 2021.
S. Najafli, A. T. Haghighat, and B. Karasfi, “Taxonomy of deep learning-based intrusion detection system approaches in fog computing: a systematic review,” Knowledge and Information Systems, vol., pp. 34, 2024.
Z. T. Pritee, M. H. Anik, S. B. Alam, J. R. Jim, M. M. Kabir, and M. F. Mridha, “Machine learning and deep learning for user authentication and authorization in cybersecurity: A state-of-the-art review,” Computers & Security, vol. 140, pp. 21, 2024.
K. Ramezanpour and J. Jagannath, “Intelligent zero trust architecture for 5G/6G networks: Principles, challenges, and the role of machine learning in the context of O-RAN,” Computer Networks, vol. 217, pp. 11, 2022.
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