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Cloud and Big Data Security System’s Review Principles: A Decisive Investigation

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Abstract

We perceive big data with massive datasets of complex and variegated structures in the modern era. Such attributes formulate hindrances while analyzing and storing the data to generate apt aftermaths. Privacy and security are the colossal perturb in the domain space of extensive data analysis. In this paper, our foremost priority is the computing technologies that focus on big data, IoT (Internet of Things), Cloud Computing, Blockchain, and fog computing. Among these, Cloud Computing follows the role of providing on-demand services to their customers by optimizing the cost factor. AWS, Azure, Google Cloud are the major cloud providers today. Fog computing offers new insights into the extension of cloud computing systems by procuring services to the edges of the network. In collaboration with multiple technologies, the Internet of Things takes this into effect, which solves the labyrinth of dealing with advanced services considering its significance in varied application domains. The Blockchain is a dataset that entertains many applications ranging from the fields of crypto-currency to smart contracts. The prospect of this research paper is to present the critical analysis and review it under the umbrella of existing extensive data systems. In this paper, we attend to critics' reviews and address the existing threats to the security of extensive data systems. Moreover, we scrutinize the security attacks on computing systems based upon Cloud, Blockchain, IoT, and fog. This paper lucidly illustrates the different threat behaviour and their impacts on complementary computational technologies. The authors have mooted a precise analysis of cloud-based technologies and discussed their defense mechanism and the security issues of mobile healthcare.

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References

  1. Ahmed, E. S. A., & Saeed, R. A. (2014). A survey of big data cloud computing security. International Journal of Computer Science and Software Engineering (IJCSSE), 3(1), 78–85.

    Google Scholar 

  2. Mishra, A. D., & Singh, Y. B. (2016). Big data analytics for security and privacy challenges. In: 2016 international conference on computing, communication and automation (ICCCA) (pp. 50–53). IEEE.

  3. Puthal, D., et al. (2017). A synchronized shared key generation method for maintaining end-to-end security of big data streams. In: 50th Hawaii international conference system of science (HICSS) pp. 6011–6020.

  4. Kum, H. C., et al. (2013). Social genome: Putting big data to work for population informatics. Computer, 47(1), 56–63.

    Article  Google Scholar 

  5. Mayank B., Monica S., & Sumit K. Y. (2015). Big data query optimization by using locality sensitive bloom filter. In: 2nd international conference on computer for sustainable global development pp. 1424–1428.

  6. Garcia-M, O., et al. (2013). Cooperative security in distributed networks. Computer Communications, 36, 1284–1297.

    Article  Google Scholar 

  7. Yu, S., et al. (2016). Networking for big data: A survey. IEEE Communications Surveys & Tutorials, 19(1), 531–549. https://doi.org/10.1109/COMST.2016.2610963

    Article  Google Scholar 

  8. Li, Y., et al. (2017). Intelligent cryptography approach for secure distributed big data storage in cloud computing. Information Sciences, 387, 103–115.

    Article  MATH  Google Scholar 

  9. Zhihui, Lu., et al. (2018). IoTDeM: An IoT Big Data-oriented MapReduce performance prediction extended model in multiple edge clouds. Journal of Parallel and Distributed Computing, 118, 316–327.

    Article  Google Scholar 

  10. Amirhossein, F., et al. (2018). Middleware technologies for cloud of things-a survey. Digital Communications and Networks, 4(3), 1–13.

    Google Scholar 

  11. Puthal, D., et al. (2016). Threats to networking cloud and edge data centres in the Internet of Things. IEEE Cloud Computing, 3(3), 64–71.

    Article  Google Scholar 

  12. Alrawais, A., et al. (2017). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet Computing, 21(2), 34–42.

    Article  Google Scholar 

  13. Dastjerdi, A. V., et al. (2016). Fog computing: Principles, architectures, and applications. In R. Buyya & A. Dastjerdi (Eds.), Internet of Things (pp. 61–75). Elsevier

  14. Rad, B. B., & Shareef, A. A. (2017). Fog computing: A short review of concept and applications. International Journal of Computer Science and Network Security, 17(11), 68–74.

    Google Scholar 

  15. Tang, B., et al. (2017). Incorporating intelligence in fog computing for big data analysis in smart cities. IEEE Transactions on Industrial Informatics, 13(5), 2140–2150.

    Article  Google Scholar 

  16. Laredo, J. L. J., et al. (2017). Load balancing at the edge of chaos: How self-organized criticality can lead to energy efficient computing. IEEE Transactions on Parallel and Distributed Systems, 28(2), 517–529.

    Article  Google Scholar 

  17. Basudan, S., Lin, X., & Sankaranarayanan, K. (2017). A privacy-preserving vehicular crowd sensing-based road surface condition monitoring system using fog computing. IEEE Internet of Things Journal, 4(3), 772–782.

    Article  Google Scholar 

  18. Fernandez-Gago, C., Moyano, F., & Lopez, J. (2017). Modelling trust dynamics in the internet of things. Information Sciences Journal, 396, 72–82.

    Article  Google Scholar 

  19. Gai, K., & Qiu, M. (2018). Optimal resource allocation using reinforcement learning for IoT content-centric services. Applied Soft Computing, 70, 12–21.

    Article  Google Scholar 

  20. Vohra, K., & Dave, M. (2018). Multi-authority attribute based data access control in fog computing. Procedia Computer Science, 132, 1449–1457.

    Article  Google Scholar 

  21. Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17–39.

    Article  Google Scholar 

  22. Hillary A., Bryan R., & Bruce M. (2017). Advanced driver assistance systems (ADAS): A consideration of driver perceptions on training, usage & implementation. In: Proceeding of the human factors and ergonomics society annual meeting Vol. 61, No. 1, pp. 1954–1958.

  23. Lukas, M., et al. (2016). On perspective of security and privacy-preserving solutions in the internet of things. Computer Networks, 102, 83–95.

    Article  Google Scholar 

  24. Rimal, B. P., Van, D. P., & Maier, M. (2017). Mobile edge computing empowered fiber-wireless access networks in the 5G era’. IEEE Communications Magazine, 55(2), 192–200.

    Article  Google Scholar 

  25. Varghese, B., et al. (2016). Challenges and opportunities in edge computing. In: 2016 IEEE international conference on smart cloud (SmartCloud) (pp. 20–26). IEEE

  26. Li, L., Li, Y., & Hou, R. (2017). A novel mobile edge computing-based architecture for future cellular vehicular networks. In: 2017 IEEE wireless communications and networking conference (WCNC) (pp. 1–6). IEEE.

  27. Gervais, A., et al., (2015). Tampering with the delivery of blocks and transactions in bitcoin. In: ACM conference on Computer and Communications Security (pp. 692–705). ACM.

  28. Sabrina, S., et al. (2017). Security towards the edge: Sticky policy enforcement for networked smart objects. Information Systems, 71, 78–89.

    Article  Google Scholar 

  29. Tao, X., & Xiao, Q. (2007). Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity. Journal of Parallel and Distributed Computing, 67, 1067–1081.

    Article  MATH  Google Scholar 

  30. Hui, G., et al. (2018). A scalable and manageable IoT architecture based on transparent computing. Journal of Parallel and Distributed Computing, 118, 5–13.

    Article  Google Scholar 

  31. Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, 27–38.

    Article  Google Scholar 

  32. Cao, Y., et al. (2015). FAST: A fog computing assisted distributed analytics system to monitor fall for stroke mitigation. In: International conference on networking, architecture and storage (NAS) (pp. 2–10).

  33. Junchao, W., et al. (2018). A novel security scheme for Body Area Networks compatible with smart vehicles. Computer Networks, 143, 74–81.

    Article  Google Scholar 

  34. David, S. J., et al. (2018). System for monitoring and supporting the treatment of sleep APNEA using IOT and big data. Pervasive and Mobile Computing, 50, 25–40.

    Article  Google Scholar 

  35. Mengmeng, G., et al. (2017). A framework for automating security analysis of the internet of things. Journal of Network and Computer Applications, 83, 12–27.

    Article  Google Scholar 

  36. Dazhong, W., et al. (2017). A fog computing-based framework for process monitoring and prognosis in cyber-manufacturing. Journal of Manufacturing Systems, 43, 25–34.

    Article  Google Scholar 

  37. Yan, S., Lin, F., & Nan, Z. (2018). A security mechanism based on evolutionary game in fog computing. Saudi Journal of Biological Sciences, 25, 237–241.

    Article  Google Scholar 

  38. Dorsemaine, B., et al. (2016). A new approach to investigate IoT threats based on a four layer model. In: IEEE conference on emerging topics in Computing NOTER (pp. 1–6).

  39. Guan, J., Wei, Z., & You, I. (2018). GRBC-based network security functions placement scheme in SDS for 5G security. Journal of Network and Computer Applications, 114, 48–56.

    Article  Google Scholar 

  40. Kajaree, D., & Behera, R. (2017). A survey on IoT security threats and solutions. International Journal of Innovative Research in Computer and Communication Engineering, 5(2), 1302–1309.

    Google Scholar 

  41. Eleni, K., et al. (2015). Elastic virtual machine placement in cloud computing network environments. Computer Networks, 93, 435–447.

    Article  Google Scholar 

  42. Jalali, F. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34, 1728–1739.

    Article  Google Scholar 

  43. Anwar, S., et al. (2017). From intrusion detection to an intrusion response system: Fundamentals, requirements, and future directions. MDPI Algorithms, 10(2), 1–24.

    Google Scholar 

  44. Maleh, Y., Abdellah, E., & Belaissaoui, M. (2016). Dos attacks analysis and improvement in dtls protocol for internet of things. In: ACM (Ed.), ACM international conference on big data and advanced wireless technologies (BDAW’2016) (pp. 1–7).

  45. Yongnan, Z., & Yonghua, Z. (2018). Distributed coordination control of traffic network flow using adaptive genetic algorithm based on cloud computing. Journal of Network and Computer Applications, 119, 110–120.

    Article  Google Scholar 

  46. Huang, Z. (2017). Insight of the protection for data security under selective opening attacks. Information Sciences, 412, 223–241.

    Article  MATH  Google Scholar 

  47. Li, J. (2018). Secure attribute-based data sharing for resource-limited users in cloud computing. Computers & Security, 72, 1–12.

    Article  Google Scholar 

  48. Maglaras, L. A., Jiang, J., & Cruz, T. J. (2016). Combining ensemble methods and social network metrics for improving accuracy of OCSVM on intrusion detection in SCADA systems. Journal of Information Security and Applications, 30, 15–26.

    Article  Google Scholar 

  49. Ibrahim, M. H. (2016). Octopus: An edge-fog mutual authentication scheme. International Journal Network Security, 18(6), 1089–1101.

    Google Scholar 

  50. Chen, M., & Leung, V. C. (2018). From cloud-based communications to cognition-based communications: A computing perspective. Computer Communications, 128, 74–79.

    Article  Google Scholar 

  51. Mouradian, C., et al. (2017). A comprehensive survey on fog computing: State-of-the-art and research challenges. IEEE Communication Surveys Tutorials, 20(1), 416–464.

    Article  Google Scholar 

  52. Egli, S., et al. (2017). A 10-year fog and low stratus climatology for Europe based on Meteosat Second Generation data. Quarterly Journal of the Royal Meteorological Society, 143, 530–541.

    Article  Google Scholar 

  53. Eva, M. T., et al. (2017). Do we all really know what a fog node is? Current trends towards an open definition. Computer Communication, 109, 117–130.

    Article  Google Scholar 

  54. Aazam, M., & Huh, E.-N. (2016). Fog computing: The cloud-IoT/IoE middleware paradigm. IEEE Potentials, 35(3), 40–44.

    Article  Google Scholar 

  55. Naranjo, P. G. V., et al. (2019). FOCAN: A Fog-supported smart city network architecture for management of applications in the Internet of Everything environments. Journal of Parallel and Distributed Computing, 132, 274–283.

    Article  Google Scholar 

  56. Pengfei, H., et al. (2017). Survey on fog computing: Architecture, key technologies, applications and open issues. Journal of Network and Computer Applications, 98, 27–42.

    Article  Google Scholar 

  57. Lu, R., et al. (2017). A lightweight privacy-preserving data aggregation scheme for Fog computing-enhanced IoT. IEEE Access, 5, 3302–3312.

    Article  Google Scholar 

  58. Yi, S., Qin, Z., & Li, Q. (2015). Security and privacy issues of fog computing: A survey. In: Proceeding of 10th international conference wireless algorithms, system, application (WASA) (pp. 685–695).

  59. Fei, H., He, J., & Wang, M. (2017). Research on fog computing based active anti-theft technology. Procedia Computer Science, 111, 209–213.

    Article  Google Scholar 

  60. de Assuncao, M. D., da Silva Veith, A., & Buyya, R. (2018). Distributed data stream processing and edge computing: A survey on resource elasticity and future directions. Journal of Network and Computer Applications, 103, 1–17.

    Article  Google Scholar 

  61. Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things Journal, 3(6), 854–864.

    Article  Google Scholar 

  62. He, Q., et al. (2017). Fog-based transcoding for crowd sourced video live cast. IEEE Communications Magazine, 55(4), 28–33.

    Article  Google Scholar 

  63. Liu, C., et al. (2011). Research on immunity-based intrusion detection technology for the internet of things. In: Proceedings of the ICNC (pp. 212–216). IEEE.

  64. Singh, D., Tripathi, G., & Jara, A. J. (2014). A survey of Internet-of-things: Future vision, architecture, challenges and services, In Internet of Things (WF-IoT). In: 2014 IEEE World Forum on (pp. 287–292).

  65. Fadele, A. A., et al. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10–28.

    Article  Google Scholar 

  66. Ganz, F., Puschmann, D., Barnaghi, P., & Carrez, F. (2015). A practical evaluation of information processing and abstraction techniques for the internet of things. IEEE Internet of Things Journal, 2, 340–354.

    Article  Google Scholar 

  67. Flavio, B., et al. (2014). Fog computing: A platform for internet of things and analytics, book on big data and internet of things: A roadmap for smart environments. pp. 169–186.

  68. Atlam, H. F., Robert, J. W., & Gary, B. W. (2018). Fog computing and the Internet of Things: A review. Big Data Cognitive Computing, 2(2), 1–18.

    Article  Google Scholar 

  69. Parvaneh, A., Amir, M. R., & Hamid, H. S. J. (2018). Service composition approaches in IoT: A systematic review. Journal Network and Computer Applications, 120, 61–77.

    Article  Google Scholar 

  70. Bull, P., et al. (2016). Flow based security for iot devices using ASDN gateway. In: 2016 IEEE 4th international conference on future internet of things and cloud future internet of things and cloud (FiCloud) (pp. 157–163).

  71. Jie, L., et al. (2017). A survey on internet of things: Architecture, enabling technologies, security and privacy, and applications. IEEE Internet of Things Journal, 4(5), 1125–1142.

    Article  Google Scholar 

  72. Sun, P., et al. (2019). Modeling and clustering attacker activities in IoT through machine learning techniques. Information Sciences, 479, 456–471.

    Article  Google Scholar 

  73. Yaqoob, I., et al. (2017). The rise of ransomware and emerging security challenges in the Internet of Things. Computer Networks, 129, 444–458.

    Article  Google Scholar 

  74. Jan, H. Z., Oscar, G. M., & Klaus, W. (2014). Privacy in the Internet of Things: Threats and challenges. Security and Communication Networks, 7(12), 2728–2742.

    Article  Google Scholar 

  75. Paul, G., Sarkar, P., & Mukherjee, S. (2014). Towards a more democratic mining in bitcoins. In: ICISS, series lecture notes in computer science (pp. 185–203).

  76. Heilman, E., Baldimtsi, F., & Goldberg, S. (2016). Blindly signed contracts: Anonymous on-blockchain and off-blockchain bitcoin transactions. In: Proceeding of the international conference on Fin Cryptogra and data section (pp. 1–6).

  77. Herbert, J., & Litchfield, A. (2015). A novel method for decentralised peer- to-peer software license validation using crypto currency Blockchain technology. In: ACSC, Series CRPIT (Vol. 159, pp. 27–35). Australian Comp Socie.

  78. Ola, S., et al. (2018). IoT survey: An SDN and fog computing perspective. Computer Networks, 143, 221–246.

    Article  Google Scholar 

  79. Ziegeldorf, J. H., et al. (2016). Secure and anonymous decentralized Bitcoin mixing, Future Gener. Computing Systems, 80, 448–466.

    Google Scholar 

  80. Singh, J., et al. (2016). Twenty security considerations for cloud supported Internet of Things. IEEE Internet of Things J., 3(3), 269–284.

    Article  Google Scholar 

  81. Li, Y., Sun, L., & Wang, W. (2014). Exploring device-to-device communication for mobile cloud computing. In: IEEE international conference on communication ICC (pp. 2239–2244).

  82. Partha, P. R. (2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1, 35–46.

    Article  Google Scholar 

  83. Rafał, K., et al. (2018). A scalable distributed machine learning approach for attack detection in edge computing environments. Journal of Parallel and Distributed Computing, 119, 18–26.

    Article  Google Scholar 

  84. Tandon, R., & Simeone, O. (2016). Harnessing cloud and edge synergies: toward an information theory of fog radio access networks. IEEE Communications Magazine, 54(8), 44–50.

    Article  Google Scholar 

  85. Salman, I., et al. (2016). On cloud security attacks: A taxonomy and intrusion detection and prevention as a service. Journal of Network and Computer Applications, 74, 98–120.

    Article  Google Scholar 

  86. Sadip, M., et al. (2018). Multi-objective optimization technique for resource allocation and taskscheduling in vehicular cloud architecture: A hybrid adaptive nature inspired approach. Journal of Network and Computer Applications, 103, 58–84.

    Article  Google Scholar 

  87. Singh, S., JeongY, S., & Jong, H. P. (2016). A survey on cloud computing security: Issues, threats, and solutions. Journal of Network and Computer Applications, 75, 200–222.

    Article  Google Scholar 

  88. Wang, T., et al. (2017). Trajectory privacy preservation based on a fog structure for Cloud location services. IEEE Access, 5, 7692–7701.

    Article  Google Scholar 

  89. da Cristiano, C. A., et al. (2018). Internet of Health Things: Toward intelligent vital signs monitoring inhospitalwards. Artificial Intelligence in Medicine, 89, 61–69.

    Article  Google Scholar 

  90. Wang, K. H., et al. (2017). A secure authentication scheme for Internet of Things. Pervasive and Mobile Computing, 42, 15–26.

    Article  Google Scholar 

  91. Sulahuddin, M. A., Al-Fuqaha, A., & Guizani, M. (2015). Software defined networking for RSU Clouds in support of the internet of vehicle. IEEE Internet of Things Journal, 2(2), 133–144.

    Article  Google Scholar 

  92. Nesrine, K., & Maryline, L. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120–141.

    Article  Google Scholar 

  93. Ye, D., Wu, M., Tang, S., & Yu, R. (2016). Scalable fog computing with service offloading in bus networks. In: IEEE 3rd international conference on cyber security and cloud computing (CSCloud) (pp. 247–251).

  94. Valenta L., Rowan B. (2015). Blindcoin: Blinded, accountable mixes for bitcoin. In: Financial Cryptography Workshops, series Lect Notes in Computer Science (Vol. 8976, pp. 112–126).

  95. Khan, S., & Parkinson, S. (2018). Eliciting and utilising knowledge for security event log analysis: An association rule mining and automated planning approach. Expert Systems with Applications, 113, 116–127.

    Article  Google Scholar 

  96. Wail, M., Yaser, K., Muneer, B. Y., & Montaha, H. K. (2017). Mining Internet of Things for intelligent objects using genetic algorithm. Computers & Electrical Engineering, 3, 1–12.

    Google Scholar 

  97. Tarek, R. S., Essa, Q. S., & Elhadi, M. S. (2018). Fog Computing: Data streaming services for mobile End-Users. Procedia Computer Science, 134, 289–296.

    Article  Google Scholar 

  98. Lina, N., et al. (2018). A privacy preserving algorithm based on R-constrained dummy trajectory in mobile social network. Procedia Computer Science, 129, 420–425.

    Article  Google Scholar 

  99. Gu, L., et al. (2015). Cost efficient resource management in fog computing supported medical cyber-physical system. IEEE Transactions on Emerging Topics in Computing, 5(1), 108–119.

    Article  Google Scholar 

  100. Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656.

    Article  Google Scholar 

  101. Barika, R. K., et al. (2018). Mist Data: leveraging mist computing for secure and scalable architecture for smart and connected health. Procedia Computer Science, 125, 647–653.

    Article  Google Scholar 

  102. Dong-hyu, K., Heejin, L., & Kwak, J. (2017). Standards as a driving force that influences emerging technological trajectories in the converging world of the Internet and things: An investigation of the M2M/IoT patent network. Research Policy, 46, 1234–1254.

    Article  Google Scholar 

  103. Nishio, T., et al. (2013). Service oriented heterogeneous resource sharing for optimizing service latency in mobile cloud. In: Proceeding of the 1ST international workshop on mobile cloud computing and networking, series MobileCloud’13 (pp. 19–26). ACM.

  104. Zhu, T., et al. (2015). Correlated differential privacy: Hiding information in non-IID data set. IEEE Transactions on Information Forensics and Security, 10(2), 229–242.

    Article  Google Scholar 

  105. Dantcheva, A., Elia, P., & Ross, A. (2016). What else does your biometric data reveal? A survey on soft biometrics. IEEE Transactions on Information Forensics and Security, 11(3), 441–467.

    Article  Google Scholar 

  106. Chen, B., & Cheng, H. H. (2010). A review of the applications of agent technology in traffic and transportation systems. IEEE Transactions on Intelligent Transportation Systems, 11(2), 485–497.

    Article  Google Scholar 

  107. Nellore, K., & Hancke, G. P. (2016). A survey on urban traffic management system using wireless sensor networks. Sensors, 16(2), 157.

    Article  Google Scholar 

  108. Chirag, P., Mahesh, G., & Atul, P. (2013). A survey paper on e-learning based learning management Systems (LMS). International Journal of Scientific & Engineering Research, 4(6), 171–176.

    Google Scholar 

  109. Feng, D., et al. (2013). Device-to-device communications underlaying cellular networks. IEEE Transactions on communications, 61(8), 3541–3551.

    Article  Google Scholar 

  110. Habibzadeh, H., et al. (2018). Sensing, communication and security planes: A new challenge for a smart city system design. Computer Networks, 144, 163–200.

    Article  Google Scholar 

  111. Min, C., et al. (2018). Cognitive internet of vehicles. Computer Communications, 120, 58–70.

    Article  Google Scholar 

  112. Truong, N. B., Lee, G. M., & Ghamri, D.Y. (2015). Software defined networking-based vehicular Adhoc Network with Fog Computing. In: 2015 IFIP/IEEE international symposium on integrated network management (pp. 1202–1207).

  113. Mora, H., et al. (2017). Distributed computational model for shared processing on Cyber-Physical System environments. Computer Communications, 111, 68–83.

    Article  Google Scholar 

  114. Lee, J., Behrad, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters, 3, 18–23.

  115. Cruz, T., et al. (2016). A cyber security detection framework for supervisory control and data acquisition systems. IEEE Transactions on Industrial Informatics, 12(6), 2236–2246.

    Article  Google Scholar 

  116. Feng, J., et al. (2017). Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Transactions on Vehicular Technology, 66(12), 10660–10675.

    Article  Google Scholar 

  117. Ni, J., Zhang, A., & Shen, X. S. (2017). Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Communications Magazine, 55(6), 146–152.

    Article  Google Scholar 

  118. Kai, K., Cong, W., & Tao, L. (2016). Fog computing for vehicular Ad-hoc networks: Paradigms, scenarios, and issues. The Journal of China Universities of Posts and Telecommunications, 23(2), 56–96.

    Article  Google Scholar 

  119. Joel, J. P. C. R., et al. (2013). Analysis of the security and privacy. Requirements of Cloud-Based Electronic Health Records Systems, 15(8), e186.

    Google Scholar 

  120. Chen, X., et al. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE Transactions on Networking, 24(5), 2795–2808.

    Article  Google Scholar 

  121. Wang, Xu., et al. (2019). Survey on blockchain for Internet of Things. Computer Cummunication, 136, 10–29.

    Google Scholar 

  122. Alicia, Y. C. T., Chin, H. O., & Azhana, A. (2015). Fall detection sensor system for the elderly. International Journal of Advanced Computer Research, 5, 1–11.

    Google Scholar 

  123. Fan, W., et al. (2017). An efficient authentication and key agreement scheme for multi-gateway wireless sensor networks in IoT deployment. Journal of Network and Computer Applications, 89, 72–85.

    Article  Google Scholar 

  124. Sharma, A. K., Ashish, R., & Sharma, V. K. (2015). Biometric system- a review. IJCSIT, 6(5), 4616–4619.

    Google Scholar 

  125. Kalra, S., & Sood, S. K. (2015). Secure authentication scheme for IoT and cloud servers. Journal of Pervasive and Mobile Computing, 24, 210–223.

    Article  Google Scholar 

  126. Aceto, G., Valerio, P., & Antonio, P. (2018). The role of Information and communication technologies in healthcare: Taxonomies, perspectives, and challenges. Journal of Network and Computer Applications, 107, 125–154.

    Article  Google Scholar 

  127. Ivan, S. (2014). Fog computing: A cloud to the ground support for small things and machine-to-machine networks. In: Australian Telecom Netw and Apps. Conf. (ATNAC) (pp. 117–122).

  128. Leonardo, M., et al. (2017). Optimized P2P streaming for wireless distributed networks. Pervasive and Mobile Computing, 42, 335–350.

    Article  Google Scholar 

  129. Azam, M., Hung, P. P., & Huh, E.-N. (2014). Smart gateway based communication for cloud of things. In: 9th IEEE int. conf. on intell sensors, sensor netw and info proc (ISSNIP) (pp. 1–6).

  130. Daniluk, K. (2015). Smart decision fog computing layer in energy-efficient multi-hop temperature monitoring system using wireless sensor network. In: FedCSIS position papers (pp. 167–172).

  131. Motlagh, N. H., Bagaa, M., & Taleb, T. (2017). UAV-based IoT platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2), 128–134.

    Article  Google Scholar 

  132. Usman, S., et al. (2016). Cloudlet deployment in local wireless networks: Motivation, architectures, applications, and open challenges. Journal of Network and Computer Applications, 62, 18–40.

    Article  Google Scholar 

  133. Hong, Y., Liu, W. M., & Wang, L. (2017). Privacy preserving smart meter streaming against information leakage of appliance status. IEEE Transactions on Information Forensics and Security, 12(9), 2227–2241.

    Article  Google Scholar 

  134. Ray, S., Mishra, K. N., & Dutta, S. (2020). Big data security issues from the perspective of IoT and cloud computing: A review. Recent Advances in Computer Science and Communications, 12(1), 1–22.

    Google Scholar 

  135. Sunyaev, A. (2020). Cloud computing. Internet Computing (pp. 195–236). Springer.

    Chapter  Google Scholar 

  136. Javed, A. R., et al. (2020). Anomaly detection in automated vehicles using multistage attention-based convolutional neural network. IEEE Transactions on Intelligent Transportation Systems, 22, 4291–4300.

    Article  Google Scholar 

  137. Ahmed, W., et al. (2021). Security in next generation mobile payment systems: A comprehensive survey. IEEE Access, 9, 115932–115950.

    Article  Google Scholar 

  138. Riaz, S., et al. (2020). Big data security and privacy: Current Challenges and future research perspective in cloud environment. In: Proceeding of international conference on information management and technology (ICIMTech-2020) (pp. 977–982).

  139. Iwendi, C., et al. (2021). Sustainable security for the internet of things using artificial intelligence architectures. ACM Transactions on Internet Technology (TOIT), 21, 1–22.

    Article  Google Scholar 

  140. Harkut, D. G. (2020). Introductory chapter cloud computing security challenges. Cloud computing security-concepts and practice. Intech Open.

    Chapter  Google Scholar 

  141. Basit, A., et al. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76, 139–154.

    Article  Google Scholar 

  142. Basit, A., et al. (2020). A novel ensemble machine learning method to detect phishing attack. In: Proceedings of the IEEE 23rd Int Multi Conf (INMIC) (pp. 1–5).

  143. Pothuganti, S. (2020). Overview on security issues in cloud computing. International Journal of Innovation Research Computer Communication Engineering, 10, 4064–4068.

    Google Scholar 

  144. Afzal, S., et al. (2021). URL deep Detect: A deep learning approach for detecting malicious URLs using semantic vector models. Journal of Network and Systems Management, 2021(29), 1–27.

    Google Scholar 

  145. Logesswari, S., et al. (2020). A study on cloud computing challenges and its mitigations. Materials Today Proceedings, 1–5.

  146. Muhammad, A., Asad, M., & Javed, A. R. (2020). Robust early stage botnet detection using machine learning. In: Proceedings of the international conference on cyber warfare and Security (ICCWS), Islamabad, Pakistan, 20–21 October 2020. (pp. 1–6).

  147. Rehman, J. A., et al. (2020). Ensemble ada-boost classifier for accurate and fast detection of botnet attacks in connected vehicles. Transmissioin Emergency Telecommunication Technology, 1–18. https://doi.org/10.1002/ett.4088

  148. Bakr, A., El-Aziz, A., & Hefny, H. A. (2019). A survey on mitigation techniques against DDoS Attacks on cloud computing architecture. International Journal of Advanced Science and Technology, 28, 187–200.

    Google Scholar 

  149. Iwendi, C., et al. (2020). Keysplitwatermark: Zero watermarking algorithm for software protection against cyber-attacks. IEEE Access, 8, 72650–72660.

    Article  Google Scholar 

  150. Chaudhary, A. (2020). Cloud security challenges in 2020. Cloud Security Alliance, 18 February 2020. Available online: https://cloudsecurityalliance.org/blog/2020/02/18/cloud-security-challenges-in-2020/ Accessed from 8 Aug 2020.

  151. Abbasi, A., et al. (2021). ElStream: An ensemble learning approach for concept drift detection in dynamic social big data stream learning. IEEE Access, 9, 66408–66419.

    Article  Google Scholar 

  152. Thabita, F., et al. (2020). Exploration of security challenges in cloud computing: Issues, threats, and attacks with their alleviating techniques. Journal of Information and Computational Science, 12, 35–57.

    Google Scholar 

  153. Namasudra, S., et al. (2020). Towards DNA based data security in the cloud computing environment. Computer Communications, 151, 539–547.

    Article  Google Scholar 

  154. Javed, A. R., et al. (2020). Detecting motion-based side-channel attack using smartphone keystrokes. Journal of Ambient Intelligence and Humanized Computing 11, 1–14. https://doi.org/10.1007/s12652-020-01770-0

  155. Hina, M., et al. (2021). SeFACED: Semantic-based forensic analysis and classification of E-Mail data using deep learning. IEEE Access, 9, 98398–98411.

    Article  Google Scholar 

  156. Mittal, M., et al. (2021). Analysis of security and energy efficiency for shortest route discovery in low-energy adaptive clustering hierarchy protocol using Levenberg-Marquardt neural network and gated recurrent unit for intrusion detection system. Transactions on Emerging Telecommunications Technologies, 32, e3997.

    Article  Google Scholar 

  157. Rehman, A., et al. (2021). CANintelliIDS: Detecting in-vehicle intrusion attacks on a controller area network using CNN and attention-based GRU. IEEE Transactions on Network Science and Engineering, 8, 1456–1466.

    Article  Google Scholar 

  158. Shahzad, F., et al. (2021). Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. TechRxiv 2021.

  159. Mohiyuddin, A., et al. (2021). Secure cloud storage for medical IoT Data using adaptive neuro-fuzzy inference system. International Journal of Fuzzy Systems, 2021, 1–13.

    Google Scholar 

  160. Abid, R., et al. (2021). An optimised homomorphic CRT-RSA algorithm for secure and efficient communication. Personal and Ubiquitous Computing, 25, 1–14.  https://doi.org/10.1007/s00779-021-01607-3

  161. Ghobaei-Arani, M. et al. (2019). Contro City: An autonomous approach for controlling elasticity using buffer management in cloud computing environment. IEEE Access, 7, 106912–106924.

  162. Shabbir, M., et al. (2021). Enhancing security of health information using modular encryption standard in mobile cloud computing. IEEE Access, 9, 8820–8834.

    Article  Google Scholar 

  163. Al-Khafajiy, M., et al. (2020). COMITMENT: A fog computing trust management approach. Journal of Parallel and Distributed Computing, 137, 1–16.

    Article  Google Scholar 

  164. Xia, T., et al. (2021). CSPM: Metamodel for handling security and privacy knowledge in cloud service development. International Journal of Systems and Software Security and Protection (IJSSSP), 12, 68–85.

    Article  Google Scholar 

  165. Chen, F., et al. (2021). IoT cloud security review: A case study approach using emerging consumer-oriented applications. ACM Computing Survey (CSUR), 54, 1–36.

    Google Scholar 

  166. Ahamad, R. Z., et al. (2021). Interference mitigation in D2D communication underlying cellular networks: Towards green energy. CMC-Computer Materials Continua, 68, 45–58.

    Article  Google Scholar 

  167. Naeem, A., et al. (2021). DARE-SEP: A hybrid approach of distance aware residual energy-efficient SEP for WSN. IEEE Transactions on Green Communications and Networking, 5, 611–621.

    Article  Google Scholar 

  168. Javed, A. R., et al. (2021). Green 5G: Enhancing capacity and coverage in device-to-device communication. Computer Materials Continua, 67, 1933–1950.

    Article  Google Scholar 

  169. Singh, A. (2019). Security concerns and countermeasures in cloud computing: A qualitative analysis. International Journal of Information Technology, 11, 683–690.

    Article  Google Scholar 

  170. Sheikh, A., Munro, M., & Budgen, D. (2019). Systematic Literature Review (SLR) of resource scheduling and security in cloud computing. International Journal of Advanced Computer Science and Applications, 10(4), 35–44.

    Article  Google Scholar 

  171. Giri, S., & Shakya, S. (2019). Cloud computing and data security challenges: a nepal case. International Journal of Engineering Trends and Technology (IJETT), 2019(67), 146–150.

    Google Scholar 

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Funding

The fund for completing this review work was given by Birla Institute of Technology, Mesra, Ranchi, India.

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  1. Department of Computer Science & Engineering, Birla Institute of Technology, Deoghar, Jharkhand, 814142, India

    KamtaNath Mishra, Vandana Bhattacharjee, Shashwat Saket & Shivam P. Mishra

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Mishra, K., Bhattacharjee, V., Saket, S. et al. Cloud and Big Data Security System’s Review Principles: A Decisive Investigation. Wireless Pers Commun 126, 1013–1050 (2022). https://doi.org/10.1007/s11277-022-09781-0

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