Security Impact of Federated and Transfer Learning on Network Management Systems with Fuzzy DEMATEL Approach
DOI: 10.23977/jaip.2023.060404 | Downloads: 13 | Views: 564
Author(s)
Safiye Turgay 1, Suat Erdoğan 2
Affiliation(s)
1 Department of Industrial Engineering, Sakarya University, Sakarya, Turkey
2 Maro International Information Technologies Consulting Development, Support Services Industry and Trade Joint Stock Company, İstanbul, Turkey
Corresponding Author
Safiye TurgayABSTRACT
Everyday using of the big data, machine learning algorithms, and related studies, ensuring data privacy and security have become a critical necessity. These features make them more vulnerable to cyber-attacks. The security of the stored data is also critical, and evaluating the processing of information in the autonomous network management of these systems. The criteria considers the account in the processing and security of data entering every field from the widespread industry examined. It is necessary to increase their awareness of negative and attack problems while these systems are working. Applications such as traditional machine learning and the use of cloud computing also involve risks regarding data security and personal data leakage. Cooperative learning pays due attention to the confidentiality of sensitive information by keeping the original training data hidden. By collecting, combining, and integrating heterogeneous data with collaborative learning together with a federated learning structure, data produced and stored. This study discusses the effect of federated and transfer learning on autonomous network management analyzes the security status parameters. The fuzzy DEMATEL method was preferred in exploring the parameters affecting the system state according to the degree of importance. Situational scenarios evaluated by considering the structure in which the features of cyber-physical systems examined together with federated learning. Data security factors discussed with the fuzzy DEMATEL
KEYWORDS
Fuzzy DEMATEL; Autonomic Network Management; Federated Learning; Transfer Learning; Security; Big DataCITE THIS PAPER
Safiye Turgay, Suat Erdoğan, Security Impact of Federated and Transfer Learning on Network Management Systems with Fuzzy DEMATEL Approach. Journal of Artificial Intelligence Practice (2023) Vol. 6: 20-30. DOI: http://dx.doi.org/10.23977/jaip.2023.060404.
REFERENCES
[1] Rahatulain A. Onori M., A new methodology to analyze the functional and physical architecture of existing products for an assembly oriented product family, Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 28th CIRP Design Conference, May 2018, Nantes, France
[2] Rahatulain A., Onori Viewpoints and views for the architecture description of cyber-physical manufacturing systems, Peer-review under responsibility of the scientific committee of the 28th CIRP Design Conference 2018. 28th CIRP Design Conference, May 2018, Nantes, France, CIRP 00 (2017) 000-0000
[3] Skowronski R., The open blockchain-aided multi agent symbiotic cyber-physical systems, Future Generation Computer Systems 94(2019) 430-443.
[4] Bolbot V., Theotokatos G., Bujorianu L.M., Boulougouris E., Vassalos D., Vulnerabilities and safety assurance methods in Cyber-Physical Systems: A comprehensive review, Reliability Engineering and System Safety, 182 (2019) 179-193.
[5] Hoffmannn R., Napiorkowski J., Prorasowicki T., Stanik J., Risk based approach in scope of cybersecurity threads and requirements, 1st International Conference on Optimization-Driven Architectural Design (OPTARCH 2019), Procedia Manufacturing 44(2020) 655-662
[6] Marotta A., Martinelli F., Nanni S. Orlando A., Yautsiukhin A, Cyber-insurance survey, Computer Science Review, 24 (2017) 35-61
[7] Ruan K., Introducing cybernomics: A unifying economic framework for measuring cyber risk, Computers&Security (2017), 83, pp.77-89
[8] Su D., Liu J., Wang W., Wang X., Du X., Guizani M., Discovering communities of malapps on Androis-based mobile cyber-physcal systems, Ad Hoc Networks, 80 (2018) 104-118
[9] Akinrolabu O., Nurse J.R.C., Martin A., New S., Cyber risk assessment in cloud provider environments: Current models and future needs, Computers & Security 87(2019) 101600.
[10] Mukhopadhyay A., Chatterjee S., Saha D., Mahanti A., Sadhukhan S.K., Cyber-risk decisison models: To insure IT or not, Decision Support Systems, 56 (2013), 11-26.
[11] Cardin O., Classification of cyber-physical production systems applications: Proposition of an analysis framework, Computers in Industry, 104(2019) 11-21
[12] Barrere M., Hankin C., Nicolaou N., Eliades D.G., Parisini T., Measuring cybephysical security in industrial control via minimum-effort attack strategies, Journal of Information Security and Applications, 52(2020), 102471
[13] Yaacoub J.P.A., Salman O., Noura H.N., Kaaniche N., Chehab A., Malli M., Cyber-physical systems security: Limitations, issues and future trends, Microprocessors and Microsystems, (2020), 77, 103201
[14] Bendiab G., Shiaeles S., Boucherkha S., FCMDT: A novel fuzzy cognitive mapsa dynamic trust model for cloud federated identity management, computer & security 86 (2019) 270-290.
[15] Tchoffa D., Figay N., Ghodous P., Panetto H., El Mhamed A., Alignment of the product lifecycle management federated framework with Internet of things and virtual manufacturing, Computers in Industry 130 (2021) 103466
[16] Polap D., Srivastava G., Yu K., Agent architecture of an intelligent medical system based on federated learning and blockchain technology, Journal of Information Security and Applications 58 (2021) 102748
[17] Xia Q., Ye W., Tao Z., Wu J., Li Q., A survey of federated learning for edge computing: Research problems and solutions, High-Confidence Computing 1 (2021) 100008
[18] Ali M., Integration of blockchain and federated learning for Internet of Things: Recent advances and future challenges, Computers&Security 108(2021) 102355
[19] Bianco-Justicia A., Domingo-Ferrer J., Martinez S., Snchez D., Flanagan A., Tan K.E., A., Achieving security and privacy in federated learning systems: Survey, research challenges and future directions, Engineering Applications of Artificial Intelligence Volume 106, November 2021, 104468
[20] Mothukuri V., Parizi R.M., Pouriyeh S., Huang Y., Dehghantanha A., Srivastava G., A survey on security and privacy of federated learning, Future Generation Computer Systems, Volume 115, February 2021, Pages 619-640.
[21] Chen M., Shlezinger N., Poor H.V., Eldar Y.C., Cui S., Communication-efficient federated learning, PNAS April 27, 2021 118 (17) e2024789118; https://doi.org/10.1073/pnas.2024789118
[22] Chen Y., Luo F., Li F., Xiang T., Liu Z., Li J.,A trsining-integrity privacy-preserving federated learning scheme with trusted execution environment, Information Sciences, Vol. 522, June 2020, pp. 69-79
[23] Gabus A., Fontela E. (1972). World Problems. An Invitation to Further Thought Within The Framework of DEMATEL. Battelle Geneva Research Centre, Geneva.
[24] Lin Chijen, Wu Weiwen (2004). A fuzzy extension of the DEMATEL method for group decision making. European Journal of Operational Research, 156, 445-455.
[25] Lin Chijen, Wu Weiwen (2008). A causal analytical method for group decision making under fuzzy environment. Expert Systems with Applications, 34, 205-213.
[26] Muhammad M.N., Cavus N., Fuzzy DEMATEL method for identifying LMS evaluation criteria, Procedia Computer Science, Volume 120, 2017, pp. 742-749
[27] Stief P., Dantan J.Y., Etienne A., Siadat A., A new methodology to analyze the functional and physical architecture of existing products for an assembly oriented product family identification, Peer-review under responsibility of the scientific committee of the 51st CIRP Conference on Manufacturing Systems. 10.1016/j.procir.2018.03.116, 51st CIRP Conference on Manufacturing Systems
[28] Uygun Ö., Turgay S. Healthcare Management Evaluatıon Wıth Fuzzy Dematel And Fuzzy Anp Approach, Proceedings of 8th International Symposium on Intelligent and Manufacturing Systems (IMS 2012) , Sakarya University Department of Industrial Engineering, Adrasan, Antalya, Turkey, September 27-28, 2012: 567-582
[29] Yager R.R., Filev D.P. (1994). Essentials of fuzzy modeling and control. New York: John Wiley & Sons.
[30] Opricovic S., Tzeng G.H. (2003). Defuzzification within a multicriteria decision nıdel, International Journal of Uncertainty. Fuzziness and Knowledge-Based Systems, 11(5), 635-652.
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