روشی ترکیبی به‌منظور شناسایی فراهم‌کنندگان خدمات ابری قابل‌اعتماد با استفاده از فرآیند تحلیل سلسله مراتبی و شبکه‌های عصبی

نوع مقاله : مقاله پژوهشی

نویسندگان

1 آزاد اسلامی واحد تبریز

2 دانشگاه آزاد اسلامی واحد تبریز

چکیده

اخیرا فناوری رایانش ابری توانسته است در مدت‌زمان کوتاهی محبوبیت گسترده­ای بیابد. لذا با ­توجه به ­این محبوبیت شمار قابلیت­ها و  ویژگی­های خدمات ابری نیز رو به افزایش می‌باشد. در محیط­های ابری به‌منظور یافتن ارائه­دهنده معتبر و انتخاب بهترین منابع در  زیرساخت­های ناهمگن ابری، اعتماد نقش مهمی را ایفا می­کند. عدم اعتماد مشتریان به ارائه­دهندگان خدمات ابری بزرگ‌ترین مانعی است که اغلب برای ­پذیرش خدمات ابری در نظر گرفته‌ می‌شود. در این پژوهش سعی بر تدوین مدل شناسایی ارائه­دهندگان خدمات ابری نامعتبر خواهد بود که با استفاده از ویژگی­های ارزیابی اعتماد به ارائه­دهندگان ابری، اعتبارسنجی انجام خواهد گرفت. در رویکرد پیشنهادی به‌منظور تشخیص فراهم­کنندگان ابری ترکیب روش شبکه عصبی با وزن­دهی سلسله ­مراتبی ارائه‌ شده است و علت به­کار گرفتن شبکه عصبی، قابلیت پیدا کردن و تشخیص مقادیر بهینه آن می‌باشد. نتایج شبیه‌سازی حاکی از آن است که درصد خطای این روش 005/0% می‌باشد که به­نسبت روش­های رایج دیگر دارای دقت بیشتری است.

کلیدواژه‌ها


[1]     F. J. Krautheim, “Building trust into utility cloud computing,” University of Maryland, Baltimore County, 2010.##
[2]     F. N. Njeh, “Cloud Computing: An Evaluation of the Cloud Computing Adoption and Use Model,” Bowie State University, 2014.##
[3]    I. M. Abbadi and M. Alawneh, “A framework for establishing trust in the Cloud,” Computers & Electrical Engineering, vol. 38, pp.              1073-1087, 2012.##
[4]     F. Krautheim, D. Phatak, and A. Sherman, “Introducing the trusted virtual environment module: a new mechanism for rooting trust in cloud computing,” Trust and Trustworthy Computing, pp. 211-227, 2010.##
[5]     N. Somu, K. Kirthivasan, and S. S. VS, “A computational model for ranking cloud service providers using hypergraph based techniques,” FutureGeneration Computer Systems, vol. 68, pp. 14-30, 2017/03/01/ 2017.##
[6]     B. Keshanchi, A. Souri, and N. J. Navimipour, “An improved genetic algorithm for task scheduling in the cloud environments using the priority queues: Formal verification, simulation, and statistical testing,” Journal of Systems and Software, vol. 124, pp. 1-21, 2017.##
[7]     F. Sheikholeslami and N. J. Navimipour, “Service allocation in the cloud environments using multi-objective particle swarm optimization algorithm based on crowding distance,” Swarm and Evolutionary Computation, vol. 35, pp. 53-64, 2017/08/01/ 2017.##
[8]     A. Vakili and N. J. Navimipour, “Comprehensive and systematic review of the service composition mechanisms in the cloud environments,” Journal of Network and Computer Applications, vol. 81, pp. 24-36, 2017/03/01/ 2017.##
[9]     M. Chiregi and N. J. Navimipour, “A comprehensive study of the trust evaluation mechanisms in the cloud computing,” Journal of Service Science Research, vol. 9, pp. 1-30, June 01 2017.##
[10]   P. Manuel, “A trust model of cloud computing based on Quality of Service,” Annals of Operations Research, vol. 233, pp. 281-292, October 01 2015.##
[11]   J. Urquhart, “The biggest cloud-computing issue of 2009 is trust,” C-Net News, vol. 7, 2009.##
[12]   G. Lin, D. Wang, Y. Bie, and M. Lei, “MTBAC: A mutual trust based access control model in Cloud computing,” China Communications, vol. 11, pp. 154-162, 2014.##
[13]   S. Jabbar, K. Naseer, M. Gohar, S. Rho, and H. Chang, “Trust model at service layer of cloud computing for educational institutes,” The Journal of Supercomputing, vol. 72, pp. 58-83, January 01 2016.##
[14]   X.-l. Xu, Q. Tu, N. Bessis, G. Yang, and X.-h. Wang, “SATVPC: Secure-agent-based trustworthy virtual private cloud model in open computing environments,” Journal of Central South University, vol. 21, pp. 3186-3196, August 01 2014.##
[15]   M. R. Mosavi, M. Khishe, “The Use of Radial Basis Function Networks Based on Leader Mass Gravitational Search Algorithm for Sonar Dataset Classification,”Journal Of Electronical & Cyber Defence, vol. 4, no. 2, 2016.(In Persian)##
 [16]  V. Viji Rajendran and S. Swamynathan, “Hybrid model for dynamic evaluation of trust in cloud services,” Wireless Networks, vol. 22, pp.    1807-1818, August 01 2016.##
[17]   H. Kim, H. Lee, W. Kim, and Y. Kim, “A trust evaluation model for QoS guarantee in cloud systems,” International Journal of Grid and Distributed Computing, vol. 3, pp. 1-10, 2010.##
[18]   Z. Yang, L. Qiao, C. Liu, C. Yang, and G. Wan, “A collaborative trust model of firewall-through based on Cloud Computing,” In The 2010 14th International Conference on Computer Supported Cooperative Work in Design, pp. 329-334, 2010.##
[19]   A. Sadeghi, M. R. Valavi, M. Asadi Vasfi, M. Barari, and G. Mohtashami,“A Method for Multilayer Computing of IT Services Availability,”Journal Of Electronical & Cyber Defence,vol. 5, no. 3, 2017.(In Persian)##
[20]   S. Haykin, “Neural networks: a comprehensive foundation: Prentice Hall PTR,” 1994.##
[21]   R. M. Mohammad, F. Thabtah, and L. McCluskey, “Predicting phishing websites based on self-structuring neural network,” Neural Computing and Applications, vol. 25, pp. 443-458, August 01 2014.##
[22]   J. Huang and D. M. Nicol, “Trust mechanisms for cloud computing,” Journal of Cloud Computing: Advances, Systems and Applications, vol. 2, p. 9, 2013.##
 
[23]   W. Fan and H. Perros, “A novel trust management framework for multi-cloud environments based on trust service providers,” Knowledge-Based Systems, vol. 70, pp.           392-406, 2014/11/01/ 2014.##
[24]   Y.-M. Wang, Y. Luo, and Z. Hua, “On the extent analysis method for fuzzy AHP and its applications,” European Journal of Operational Research, vol. 186, pp. 735-747, 2008/04/16/ 2008.##
[25]   H. Shakeri, A. G. Bafghi, and H. S. Yazdi, “Computing trust resultant using intervals,” in 2011 8th International ISC Conference on Information Security and Cryptology, pp. 15-20, 2011.##
[26]   J. Leonard and M. A. Kramer, “Improvement of the backpropagation algorithm for training neural networks,” Computers & Chemical Engineering, vol. 14, pp. 337-341, 1990/03/01/ 1990.##
[27]   G. Ramesh, I. Krishnamurthi, and K. S. S. Kumar, “An efficacious method for detecting phishing webpages through target domain identification,” Decision Support Systems, vol. 61, pp. 12-22, 2014/05/01/ 2014.##
[28]   L. A. T. Nguyen, B. L. To, H. K. Nguyen, and M. H. Nguyen, “An efficient approach for phishing detection using single-layer neural network,” In 2014 International Conference on Advanced Technologies for Communications (ATC 2014), pp. 435-440, 2014.##
[29]   K. Quinn, D. Lewis, D. O’Sullivan, and V. P. Wade, “An analysis of accuracy experiments carried out over of a multi-faceted model of trust,” International Journal of Information Security, vol. 8, pp. 103-119, April 01 2009.##
[30]   K. Mahmood, A. Zidouri, and A. Zerguine, “Performance analysis of a RLS-based          MLP-DFE in time-invariant and time-varying channels,” Digital Signal Processing, vol. 18, pp. 307-320, 2008/05/01/ 2008.##