Machine Learning Approach for Cloud Data Analytics in IoT

10. Tuli, S., Basumatary, N., Gill, S.S., Kahani, M., Arya, R.C., Wander, G.S., Buyya, R., Healthfog: An ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated iot and fog computing environments. Future Gener. Comput. Syst. , 104, 187–200, 2020.

11. Liang, F., Hatcher, W.G., Xu, G., Nguyen, J., Liao, W., Yu, W., Towards online deep learning-based energy forecasting. 2019 28th International Conference on Computer Communication and Networks (ICCCN) , IEEE, pp. 1–9, 2019.

12. Ren, J., Wang, H., Hou, T., Zheng, S., Tang, C., Federated learning-based computation offloading optimization in edge computing-supported internet of things. IEEE Access , 7, 69194–69201, 2019.

13. Verma, A. and Ranga, V., Machine learning based intrusion detection systems for IoT applications. Wireless Pers. Commun., 111, 4, 2287–2310, 2020.

14. Msadek, N., Soua, R., Engel, T., Iot device fingerprinting: Machine learning based encrypted traffic analysis. 2019 IEEE Wireless Communications and Networking Conference (WCNC) , IEEE, pp. 1–8, 2019.

15. Tuli, S., Basumatary, N., Buyya, R., Edgelens: Deep learning-based object detection in integrated iot, fog and cloud computing environments. 2019 4th International Conference on Information Systems and Computer Networks (ISCON) , IEEE, pp. 496–502, 2019.

16. Luo, X.J., Oyedele, L.O., Ajayi, A.O., Monyei, C.G., Akinade, O.O., Akanbi, L.A., Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands. Adv. Eng. Inf. , 41, 100926, 2019.

17. Zafar, S., Jangsher, S., Bouachir, O., Aloqaily, M., Othman, J.B., QoS enhancement with deep learning-based interference prediction in mobile IoT. Comput. Commun. , 148, 86–97, 2019.

18. Min, Q., Lu, Y., Liu, Z., Su, C., Wang, B., Machine learning based digital twin framework for production optimization in petrochemical industry. Int. J. Inf. Manage. , 49, 502–519, 2019.

19. Garg, S., Kaur, K., Kumar, N., Kaddoum, G., Zomaya, A.Y., Ranjan, R., A hybrid deep learning-based model for anomaly detection in cloud datacenter networks. IEEE Trans. Netw. Serv. Manage. , 16, 3, 924–935, 2019.

20. Tiwari, R., Sharma, N., Kaushik, I., Tiwari, A., Bhushan, B., Evolution of IoT & Data Analytics using Deep Learning. 2019 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS) , IEEE, pp. 418–423, 2019.

21. Sujatha, R., Nathiya, S., Chatterjee, J.M., Clinical Data Analysis Using IoT Data Analytics Platforms, in: Internet of Things Use Cases for the Healthcare Industry , pp. 271–293, Springer, Cham, 2020.

22. Potluri, S., Health record data analysis using wireless wearable technology device. JARDCS , 10, 9, 696–701, 2018.

23. Mangla, M., Akhare, R., Ambarkar, S., Context-Aware Automation Based Energy Conservation Techniques for IoT Ecosystem, in: Energy Conservation for IoT Devices , pp. 129–153, Springer, Singapore, 2019.

24. Akhare, R., Mangla, M., Deokar, S., Wadhwa, V., Proposed Framework for Fog Computing to Improve Quality-of-Service in IoT Applications, in: Fog Data Analytics for IoT Applications , pp. 123–143, Springer, Singapore, 2020.

25. Potluri, S., IOT Enabled Cloud Based Healthcare System Using Fog Computing: A Case Study. J. Crit. Rev. , 7, 6, 1068–1072, 2020.

26. Chatterjee, J., IoT with Big Data Framework using Machine Learning Approach. Int. J. Mach. Learn. Networked Collab. Eng. , 2, 02, 75–85, 2018.

27. Chatterjee, J.M., Priyadarshini, I., Le, D.N., Fog Computing and Its security issues, in: Security Designs for the Cloud, Iot, and Social Networking , pp. 59–76, 2019.

28. Shri, M.L., Devi, E.G., Balusamy, B., Chatterjee, J.M., Ontology-Based Information Retrieval and Matching in IoT Applications, in: Natural Language Processing in Artificial Intelligence , pp. 113–130, Apple Academic Press, India, 2020.

29. Kumar, A., Payal, M., Dixit, P., Chatterjee, J.M., Framework for Realization of Green Smart Cities Through the Internet of Things (IoT), in: Trends in Cloud-based IoT , pp. 85–111, Springer, Cham, 2020.

30. Sujatha, R., Nathiya, S., Chatterjee, J.M., Clinical Data Analysis Using IoT Data Analytics Platforms, in: Internet of Things Use Cases for the Healthcare Industry , pp. 271–293, Springer, Cham, 2020.

31. Priya, G., Shri, M.L., GangaDevi, E., Chatterjee, J.M., IoT Use Cases and Applications, in: Internet of Things Use Cases for the Healthcare Industry , pp. 205–220, Springer, Cham, 2020.

32. Raj, P., Chatterjee, J.M., Kumar, A., Balamurugan, B., Internet of Things Use Cases for the Healthcare Industry , Springer International Publishing, India, 2020.

33. Garg, S., Chatterjee, J.M., Le, D.N., Implementation of Rest Architecure-Based Energy-Efficient Home Automation System , Security Designs for the Cloud, Iot, and Social Networking, 143–152, 2019.

34. Almusaylim, Z.A. and Zaman, N., A review on smart home present state and challenges: linked to context-awareness internet of things (IoT). Wireless networks , 25, 6, 3193–3204, 2019.

35. Almulhim, M. and Zaman, N., Proposing secure and lightweight authentication scheme for IoT based E-health applications. 2018 20th International Conference on Advanced Communication Technology (ICACT) , IEEE, pp. 481–487, 2018, February.

36. Almulhim, M., Islam, N., Zaman, N., A Lightweight and Secure Authentication Scheme for IoT Based E-Health Applications. Int. J. Comput. Sci. Netw. Secur. , 19, 1, 107–120, 2019.

37. Alshammari, M.O., Almulhem, A.A., Zaman, N., Internet of Things (IoT): Charity Automation. Int. J. Adv. Comput. Sci. Appl. (IJACSA) , 8, 2, 166–170, 2017.

38. Mangla, M. and Sharma, N., Fuzzy Modelling of Clinical and Epidemiological Factors for COVID-19, Research Square, 1, 1–15, 2020.

39. Potluri, S., An IoT based solution for health monitoring using a body-worn sensor enabled device. JARDCS , 10, 9, 646–651, 2018.

40. Potluri, S., Health record data analysis using wireless wearable technology device. JARDCS , 10, 9, 696–701, 2018.

1 *Corresponding author: deepika.rbp@gmail.com

Suchismita Sahoo1* and Sushree Sangita Sahoo2

1Biju Patnaik University of Technology, Rourkela, India

2Department of Computer, St. Paul’s School (ICSE), Rourkela, India

Abstract

Today’s era, which is being ruled by Internet of Things (IoT) or the reformation; being the Internet of Everything, has combined various technological affirmations with it. But along with its deployment, it is also undergoing malicious threats to compromise on the security issues of the IoT devices with high priority over the cloud, hence proving to be the weakest link of today’s computational intelligence infrastructure. Digital network security issue has become the desperate need of the hour to combat cyber attack. Although there have been various learning methods which have made break through, this chapter focuses on machine learning being used in cyber security to deal with spear phishing and corrosive malwares detection and classification. It also looks for the ways to exploit vulnerabilities in this domain which is invading the training data sets with power of artificial intelligence. Cloud being an inherent evolution, so as to deal with these issues, this chapter will be an approach to establish an interactive network, cognitively intervening the domains of cyber security services to the computational specifications of IoT.