Machine Learning Approach for Cloud Data Analytics in IoT. Группа авторов

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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.

      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: [email protected]

      Machine Learning for Cyber-Immune IoT Applications

       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.

      Keywords: Cyber security, machine learning, malware detection, classification

      This chapter is structured with an overview that “It’s only when they go wrong that machines remind you how powerful they are” by Clive James.

      It is a matter of great concern that, as we are progressively moving ahead with highly advanced computing technologies being deduced over internet, at the same time, the perception that is being provoked upon the security risks hovering over World Wide Web is a matter to be explored. Several encryption technologies are fuelling the online gambling and fraudulence, which is hampering the transformation of secret messages over internet.

      Hence, to fine-tune the exploitation and get a makeover, the concept of cyber security needs


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