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Fog-Assisted Secure Data Exchange for Examination and Testing in E-learning System

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Abstract

E-learning systems are getting growing interest due to their wide applicability in distance education. A huge amount of data is shared among students, teachers, examiners that should be exchanged in a confidential manner. In literature, a number of related clustering-based schemes are explored that consider security but still there is a need for dependable secure schemes. This paper explores a Secure E-learning System (SES) for sharing examinations related materials by ensuring protection against various security attacks. Exam materials include tests, quizzes, question papers, answer sheets, and aptitude tests. In the first phase, we present a secure authentication mechanism for students and teachers with a trusted server or a fog server. Next, we present a Session Key Establishment Protocol (SKEP) to setup keys for a specified time period such as a class, seminar or exam. We have also maintained the level of trust and authentication level to regularly check the legitimacy of the students. A security analysis is performed to highlight the pros and cons of security schemes to ensure reliable security for e-learning systems. We have setup a testbed using web-services in ASP.net and C# on windows Azure cloud for an e-learning scenario. Results demonstrate the effectiveness of the proposed SES in terms of reducing number of untrusted students, exams exposed, student interaction time, authentication level, reputation and trust levels for students.

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References

  1. Mehmood R, Alam F, Albogami N, Katib I, Albeshri A, Altowaijri S (2017) UTiLearn: a personalised ubiquitous teaching and learning system for smart societies. IEEE Access 5:2615–2635

    Article  Google Scholar 

  2. Mehmood R, See S, Katib I, Chlamtac I (2019) Smart infrastructure and applications: foundations for smarter cities and societies. Springer, Cham, Switzerland

    Google Scholar 

  3. Mehmood R, Bhaduri B, Katib I, Chlamtac I (2018) Smart societies infrastructure technologies and applications, vol 224. Springer, Cham, Switzerland

    Book  Google Scholar 

  4. Chen Y, He W (2013) Security risks and protection in online learning: a survey. Int Rev Res Open Dist Learn 14(5):108–127

    Google Scholar 

  5. Alam F, Mehmood R, Katib I, Albogami N, Albeshri A (2017) Data fusion and IoT for smart ubiquitous environments: a survey. IEEE Access 5:9533–9554

    Article  Google Scholar 

  6. Stergiou C, Plageras A, Psannis KE, Gupta B (2019) Secure machine learning scenario from big data in cloud computing via internet of things network. Springer, Handbook of Computer Networks and Cyber Security: Principles and Paradigms, Multimedia Systems and Applications

  7. Farouk E-SH, Al TA, Alghatani K, El-Seoud AS (2013) The impact of cloud computing technologies in E-learning. International Journal of Emerging Technologies in Learning (iJET) 8:37–43

    Article  Google Scholar 

  8. Muhammed T, Mehmood R, Albeshri A, Katib I (2018) UbeHealth: a personalized ubiquitous cloud and edge-enabled networked healthcare system for smart cities. IEEE Access 6:32258–32285

    Article  Google Scholar 

  9. Chiang M, Zhang T (2016) Fog and IoT: an overview of research opportunities. IEEE Internet Things J 3(6):854–864

    Article  Google Scholar 

  10. Atlam H, Walters R, Wills G (2018) Fog computing and the internet of things: a review. Big Data Cognit Comput 2(2):1–18

    Article  Google Scholar 

  11. Ullah A, Xiao H, Lilley M, Barker T (2014) Privacy and usability of image and text based challenge questions authentication in online examination. International Conference on Education Technologies and Computers (ICETC): 24–29, Lodz, Poland

  12. Zheng M, Bender D (2019) Evaluating outcomes of computer-based classroom testing: student acceptance and impact on learning and exam performance. Med Teacher 41(1):75–82

    Article  Google Scholar 

  13. Moubayed A, Injadat M, Ali BN, Hanan L, Shami A (2018) E-learning: challenges and research opportunities using machine learning & data analytics. IEEE Access 6:39117–39138

    Article  Google Scholar 

  14. Kausar S, Huahu X, Hussain I, Wenhao Z, Zahid M (2018) Integration of data mining clustering approach in the personalized E-learning system. Special Sect AI-Driven Big Data Process: Theory, Method Applic 6:72724–72734

    Google Scholar 

  15. Alhamdani W (2013) Secure e-learning and cryptography. IGI Global, USA p. 369

    Google Scholar 

  16. Ghafoor AS, Rajper S (2016) E-learners’ behavioral analysis using clustering technique. Int J Comput Sci Inform Sec (IJCSIS) 14(10):377–381

    Google Scholar 

  17. Wu Q, Zhan C (2016) Clustering of online learning resources via minimum spanning tree. Asian Assoc Open Univ J 11(2):197–215

    Article  Google Scholar 

  18. M. Hussain, W. Zhu, W. Zhang and R. A. S. Muhammad, "Student Engagement Predictions in an e-Learning System and Their Impact on Student Course Assessment Scores," Hindawi Computational Intelligence and Neuroscience: 1–22, 2 Octobar 2018

  19. Shovon IHM, Haque M (2012) An approach of improving Student’s academic performance by using K-means clustering algorithm and decision tree. Int J Adv Comput Sci Appl 3(8):146–149

    Google Scholar 

  20. Pecori R (2018) Virtual learning architecture enhanced by fog computing and big data streams. Future Internet 10(4):1–18

    Google Scholar 

  21. Ahmad PK, Tanika DS, Indrayadi Y (2015) Developing E-learning system to support teaching and learning activities using DSDM approach. performa 14(1):41–52

    Google Scholar 

  22. AjazMoharkan Z, Choudhury T, Chand GSRG (2017) Internet of Things and its applications in E-learning. 3rd IEEE International Conference on Computational Intelligence and Communication Technology: 1–5, Ghaziabad, India

  23. Pinjari H, Paul A, Jeon G, Rho S (2018) Context-Driven Mobile Learning Using Fog Computing,," in International Conference on Platform Technology and Service (PlatCon), Jeju: 1–6

  24. Yamada M, Cuka M, Liu Y, da Matsuo TK, Barolli L (2017) Performance evaluation of an IoT-Based E-Learning Testbed Using Mean-shift Clustering Approach Considering Delta Type of Brain Waves. 31st International Conference on Advanced Information Networking and Applications Workshops: 265–270, Taipei

  25. Matsuo K, Yamada M, Bylykbashi K, Cuka M, Liu Y, Barolli L (2018) Implementation of an IoT-Based E-learning Testbed: Performance Evaluation Using Mean-Shift Clustering Approach Considering Four Types of Brain Waves. 32nd International Conference on Advanced Information Networking and Applications Workshops: 203–209, Krakow, Poland

  26. Aissaoui K, Azizi M (2017) El-Security: E-learning Systems Security Checker Plug-in. Proceedings of the 2nd international Conference on Big Data, Cloud and Applications: 1–6, Tetouan, Morocco

  27. Luminita CD (2011) Information security in E-learning platforms. Procedia - Social Behav Sci 15:2689–2693

    Article  Google Scholar 

  28. Najwa HAM, Fan I-S (2010) E-learning and information security management. Int J Digit Soc 1(2):148–156

    Article  Google Scholar 

  29. Barik N, Karforma S (2012) Risks and remedies in E-learning system. Int J Netw Sec Applic 4(1):51–59

    Google Scholar 

  30. Savulescu C, Polkowski Z, Cosmin D, Elena B (2015) Security in e-learning systems. 7th International Conference on Electronics, Computers and Artificial Intelligence (ECAI): 19–24, Bucharest, Romania

  31. Singh P, Shende P (2014) Symmetric key cryptography: current trends. Int J Comput Sci Mob Comput 3(12):410–412

    Google Scholar 

  32. Thakur J, Kumar N (2011) DES, AES and blowfish: symmetric key cryptography algorithms simulation based performance analysis. Int J Emerg Technol Adv Eng 1(2):6–12

    Google Scholar 

  33. Dutt A, Aghabozrgi S, Maizatul AIB, Mahroeian H (2015) Clustering algorithms applied in educational data mining. Int J Inform Electron Eng 5(2):112–116

    Google Scholar 

  34. Raymond JH (2014) Promoting Education: A State of the Art Machine Learning Framework for Feedback and Monitoring E-Learning Impact. IEEE Global Humanitarian Technology Conference - South Asia Satellite: 251–254, Trivandrum, India

  35. Moreira D-OPJ, Silva ML, Freitas VD, Marçal VP, Gasparini I, Amaral MA (2002) AdaptWeb: an adaptive web-based courseware. Porto Alegre, Brazil

  36. Schiaffino S, Garcia P, Amandi A (2008) eTeacher: providing personalized assistance to e-learning students. Comput Educ 51(4):1744–1754

    Article  Google Scholar 

  37. Gasparini I, Pimenta M, Eyharabide V, Amandi A (2012) Improving user profiling for a richer personalization: modeling context in e-learning. Intelligent and Adaptive Learning Systems: Technology Enhanced Support for Learners and Teachers, IGI Global: 182–197

  38. Obasa AI, Eludire AA, Ajao TA (2013) A comparative study of synchronous and asynchronous E-learning resources. Int J Innov Res Sci Eng Technol 2(11):5938–5946

    Google Scholar 

  39. Tabak F, Rampal R (2014) Synchronous e-learning: reflections and design considerations. Int J Educ Dev Using Inf Commun Technol 10(4):80–92

    Google Scholar 

  40. Akram A, Fu C, Tang Y, Jiang Y, Lin X (2016) Exposing the hidden to the eyes: Analysis of SCHOLAT E-Learning data. IEEE 20th International Conference on Computer Supported Cooperative Work in Design: 693–698, Nanchang, China

  41. Appiahene P, Serwah S, Mensah C, Bryce K-Y (2018) Application of Wireless Ad-Hoc Networks Model to provide Education to rural Communities in Ghana. International Coference on Applied Sciences and Technology, pp. 114–121, Kumasi-Ghana

  42. Beaudin S, Levy Y, Parrish J, Danet T (2016) An empirical study of authentication methods to secure e-learning system activities against impersonation fraud. Online J Appl Knowl Manag: 42–61

  43. Alajmi QA, Kamaludin A, Abdullah AR, Al-Sharafi MA (2018) The effectiveness of cloud-based E-learning towards quality of academic services: an Omanis’ expert view. Int J Adv Comput Sci Appl 9(4):158–164

    Google Scholar 

  44. Kashyap R (2019) Biometric authentication techniques and E-learning. Biometric Authentication in Online Learning Environments: 1–30

  45. Khlifi Y, El-Sabagh H (2017) A novel authentication scheme for E-assessments based on student behavior over E-learning platform. iJET 12(4):62–89

    Google Scholar 

  46. Fadhel N, Wills G, Argles D (2011) Transparent authentication in E-learning. International Conference on Information Society: 336–342, London, UK

  47. Okada A, Whitelock D, Holmes W, Edwards C (2019) E-authentication for online assessment: a mixed-method study. Br J Educ Technol 50(2):861–875

    Article  Google Scholar 

  48. Schneier B (1996) Applied cryptography, John Wiley & Sons

  49. Jayasena K, Song H (2017) Private cloud with E-learning for resources sharing in university environment. In: Vincenti G, Bucciero A, Helfert M, Glowatz M (eds) E-learning, E-Education, and Online Training. Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering. Springer, Cham, pp 169–180

    Chapter  Google Scholar 

  50. El-Sofany H, El-Seoud S, Farouk R (2019) Studying the effect of using E-learning through secure cloud computing systems. In: Auer M, Tsiatsos T (eds) The challenges of the digital transformation in education. ICL 2018. Advances in Intelligent Systems and Computing. Springer, Cham

    Google Scholar 

  51. Schiavone E, Ceccarelli A, Bondavalli A (2016) Continuous authentication and non-repudiation for the security of critical systems. IEEE 35th Symposium on Reliable Distributed Systems, Budapest, Hungary

  52. Suciu G, Anwar M, Istrate C (2019) Mobile application and Wi-Fi network security for e-learning platforms. The International Scientific Conference eLearning and Software for Education: 393–399, Bucharest

  53. Bhatia M, Maitra JK (2018) E-learning Platforms Security Issues and Vulnerability Analysis. 2018 International Conference on Computational and Characterization Techniques in Engineering & Sciences (CCTES): 276–285, Lucknow, India

  54. Sahaya SJG, Seldev CC (2018) Secure cloud data storage approach in e-learning systems. Clust Comput: 1–6

  55. Siddiqui S, Alam S, Zam K, Gupta A (2019) Cloud-based E-learning: using cloud computing platform for an effective E-learning. In: Tiwari S, Trivedi M, Mishra K, Misra A, Kumar K (eds) Smart Innovations in Communication and Computational ScieSciences. Advances in Intelligent Syst & Computing. Springer, Singapore, pp 335–346

    Google Scholar 

Download references

Funding

This work was supported in part by the National Natural Science Foundation of China under Grant 61,572,434 and Grant 91,630,206, and in part by the Shanghai Science and Technology Committee under Grant 16DZ2293600.

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Correspondence to Xu Huahu or Ata Ullah.

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Kausar, S., Huahu, X., Ullah, A. et al. Fog-Assisted Secure Data Exchange for Examination and Testing in E-learning System. Mobile Netw Appl 28, 673–689 (2023). https://doi.org/10.1007/s11036-019-01429-x

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