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Cited by 4Network Security and Intrusion DetectionCybersecurity and Information SystemsAI and Big Data Applications

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Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence

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Ding, Jun, Alroobaea, Roobaea, Baqasah, Abdullah M, Althobaiti, Anas, Miglani, Rajan·2022·Informatica
DOI 10.31449/inf.v46i3.3866
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Abstract

In order to explore the intelligent collection of big data and network fault analysis, this paper proposes a big data intelligent collection and network fault analysis based on artificial intelligence. The construction of enterprise level information security situation awareness system is studied, and the model, architecture and specific implementation methods of information security are proposed. Through the design and deployment of the system, the effective detection, threat perception, risk determination and threat tracing of information security threats can be realized in enterprises, and the detection ability of enterprises to deal with security threats and security attacks can be comprehensively improved. The experimental results show that: Through this platform, the unknown threats analyzed by big data in the system can be manually intervened by professionals to make fine analysis, confirm attack means, target and purpose, and restore the full picture of the attacker through artificial intelligence combined with big data knowledge and multi-dimensional features of the attacker. Including homologous Trojan programs and malicious servers with different program forms, coding styles and attack principles, they "track" attackers through the overall features, continuously discover unknown threats, and ultimately ensure the accuracy of unknown threats discovery, and generate threat intelligence for the local analysis platform. It is proved that the intelligent acquisition of big data by artificial intelligence can effectively analyze network faults

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Research works printed but not linked: 32

  1. Guo, A. , & Yuan, C. . (2021). Network intelligent control and traffic optimization based on sdn and artificial intelligence. Electronics, 10(6), 700.
  2. Hu, H. , Bo, T. , Gong, X. , Wei, W. , & Wang, H. . (2017). Intelligent fault diagnosis of the high-speed train with big data based on deep neural networks. IEEE Transactions on Industrial Informatics, 13(4), 2106-2116.
  3. Juying, Dai, Jian, Tang, Shuzhan, & Huang, et al. (2019). Signal-based intelligent hydraulic fault diagnosis methods: review and prospects. Chinese Journal of Mechanical Engineering, v.32(05), 11-32.
  4. Cheng, L. , & Yu, T. . (2018). Dissolved gas analysis principle-based intelligent approaches to fault diagnosis and decision making for large oil-immersed power transformers: a survey. Energies, 11(4), 913.
  5. Wang, J. , Wang, D. , Wang, S. , Li, W. , & Song, K. . (2021). Fault diagnosis of bearings based on multi-sensor information fusion and 2d convolutional neural network. IEEE Access, PP(99), 1-1.
  6. Zhan, J. , Wang, R. , Yi, L. , Wang, Y. , & Xie, Z. . (2019). Health assessment methods for wind turbines based on power prediction and mahalanobis distance. International Journal of Pattern Recognition & Artificial Intelligence, 33(2), 1951001.1-1951001.17.
  7. Mengyuan, H. , D Qiaolin, Shutao, Z. , & Yao, W. . (2017). Research of circuit breaker intelligent fault diagnosis method based on double clustering. Ieice Electronics Express, 14(17), 20170463-20170463.
  8. Zhang, Z. . (2020). Big data analysis with artificial intelligence technology based on machine learning algorithm. Journal of Intelligent and Fuzzy Systems, 39(5), 1-8.
  9. Karim, A. H. , Hassaan, G. A. , & Hegazy, A. . (2021). Artificial neural network based intelligent fault identification of rotating machinery. International Journal of Web Engineering and Technology, 2(No 6,), 26-39.
  10. Hussein, H. A. T. , Ammar, M. E. , & Hassan, M. A. M. . (2017). Three phase induction motors stator turns fault analysis based on artificial intelligence. International Journal of System Dynamics Applications, 6(3), 1-19.
  11. Raisan, A. , Yaacob, M. M. , & Alsaedi, M. A. . (2015). Faults diagnosis and assessment of transformer insulation oil quality: intelligent methods based on dissolved gas analysis a-review. International Journal of Engineering & Technology, 4(1), 54.
  12. Lei, Y. , Jia, F. , Lin, J. , Xing, S. , & Ding, S. X. . (2016). An intelligent fault diagnosis method using unsupervised feature learning towards mechanical big data. IEEE Transactions on Industrial Electronics, 63(5), 3137-3147.
  13. Xia, M. , Li, T. , Liu, L. , Xu, L. , & Silva, C. . (2017). An intelligent fault diagnosis approach with unsupervised feature learning by stacked denoising autoencoder. IET Science Measurement ? Technology, 11(6), 687-695.
  14. Liu, Z. , Dai, C. , Hu, K. , & He, S. . (2016). A new search algorithm of mbd based on spider web and its application in power distribution network fault diagnosis. International Journal of Artificial Intelligence Tools, 25(02), 1650002.
  15. Wang, X. , Su, Y. , Li, Q. , & Han, F. . (2021). Research on intelligent operation and maintenance management method of enterprise it. Journal of Physics: Conference Series, 1732(1), 012059 (7pp).
  16. Yang, Z. , & Yin, R. . (2017). Design and research of electronic circuit fault diagnosis based on artificial intelligence. Revista de la Facultad de Ingenieria, 32(12), 766-772.
  17. Qiang, L. I. , Wenbin, W. , & Xue, L. . (2015). Intelligent recognition of axis orbits with fish-based algorithms and neural networks with mentors. Shuili Fadian Xuebao/Journal of Hydroelectric Engineering, 34(6), 191-196.
  18. Stéfano Frizzo Stefenon, Silva, M. C. , Bertol, D. W. , Meyer, L. H. , & Nied, A. . (2019). Fault diagnosis of insulators from ultrasound detection using neural networks. Journal of Intelligent and Fuzzy Systems, 37(5), 6655-6664.
  19. Chen, L. , Lan, S. , & Jiang, S. . (2019). Elevators fault diagnosis based on artificial intelligence. Journal of Physics: Conference Series, 1345(4), 042024 (10pp).
  20. Noureldeen, O. , Hamdan, I. , & Hassanin, B. . (2019). Design of advanced artificial intelligence protection technique based on low voltage ride-through grid code for large-scale wind farm generators: a case study in egypt. SN Applied Sciences, 1(6), 515-.
  21. Liu, Q. , & Huang, Z. . (2020). Research on intelligent prevention and control of covid-19 in china's urban rail transit based on artificial intelligence and big data. Journal of Intelligent and Fuzzy Systems, 39(21), 1-6.
  22. Xu, K. , Li, S. , Li, R. , Lu, J. , & Zeng, M. . (2021). Deep domain adversarial method with central moment discrepancy for intelligent transfer fault diagnosis. Measurement Science and Technology, 32(12), 124005 (16pp).
  23. M. Hedabou. Some Ways to secure elliptic curves cryptosystems. In Journal of Advances in Cliford Algebras, Vol 18, pp 677-688 , 2008
  24. A. Bentajer, M. Hedabou,K. Abouelmehdi, S. ELFEZAZI. CS-IBE : A Data Confidentiality System in Public Cloud Storage System. In Procedia Computer Science, vol 141, pp. 559-564. Elsevier, 2018.
  25. Samara, S. , & Natsheh, E. . (2020). Intelligent pv panels fault diagnosis method based on narx network and linguistic fuzzy rule-based systems. Sustainability, 12(5), 2011.
  26. Lee, K. , Han, S. , Pham, V. H. , Cho, S. , & Lee, S. W. . (2021). Multi-objective instance weighting-based deep transfer learning network for intelligent fault diagnosis. Applied Sciences, 11(5), 2370.
  27. Li, S. , & Zhou, D. . (2016). Study on a new fault diagnosis method based on combining intelligent technologies. International Journal of Multimedia and Ubiquitous Engineering, 11(6), 61-72.
  28. M. Hedabou. Cryptography for addressing Cloud Computing Security, Privacy and Trust Issues. Book on Computer and Cyber Security: Principles, Algorithm, Applications and Perspective. CRC Press, Francis and Taylor Publisher. USA, 2018
  29. P. Ahmad et al., "MH UNet: A Multi-Scale Hierarchical Based Architecture for Medical Image Segmentation," in IEEE Access, vol. 9, pp. 148384-148408, 2021, doi: 10.1109/ACCESS.2021.3122543 · DOI
  30. M. A. Razzaq et al., "The 3-Axis Scalable Service-Cloud Resource Modeling for Burst Prediction Under Smart Campus Scenario," in IEEE Access, vol. 9, pp. 116927-116941, 2021, doi: 10.1109/ACCESS.2021.3105539. · DOI
  31. Abbas, A., Alroobaea, R., Krichen, M. et al. Blockchain-assisted secured data management framework for health information analysis based on Internet of Medical Things. Pers Ubiquit Comput (2021). https://doi.org/10.1007/s00779-021-01583-8 · DOI
  32. Vol. 46 No. 3 (2022)

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Metadata

Authors
Ding, J., Alroobaea, R., Baqasah, A. M., Althobaiti, A., & Miglani, R.
Affiliations
  • Anhui Technical College of Industry and Economy (CN)
  • Taif University (SA)
  • University of Bristol (GB)
  • Lovely Professional University (IN)
ORCID
Venue
Informatica
Publisher
Slovenian Association Informatika
Published
2022-09-15
Volume
46
Issue
3
ISSN
  • 0350-5596 (ISSN-L)
  • 1854-3871
DOI
10.31449/inf.v46i3.3866
OpenAlex
W4313022491
Cited by
4
References
0
Citations by year
2026: 2 · 2025: 1 · 2024: 1
FWCI
0.21
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Informatica (publishedVersion, cc-by)
  • Informatica (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computer Networks and Communications › Network Security and Intrusion Detection
  • Physical Sciences › Computer Science › Computer Networks and Communications › Cybersecurity and Information Systems
  • Physical Sciences › Computer Science › Information Systems › AI and Big Data Applications
Keywords
enterprise information security, security situation awareness, threat detection, threat intelligence, malicious server

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Ding, J., Alroobaea, R., Baqasah, A. M., Althobaiti, A., & Miglani, R. (2022). Big Data Intelligent Collection and Network Failure Analysis Based on Artificial Intelligence. Informatica. https://doi.org/10.31449/inf.v46i3.3866