Skip to main content
Top
Published in: Wireless Personal Communications 2/2022

10-06-2022

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

Authors: KamtaNath Mishra, Vandana Bhattacharjee, Shashwat Saket, Shivam P. Mishra

Published in: Wireless Personal Communications | Issue 2/2022

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

Literature
1.
go back to reference 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. 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.
2.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Kum, H. C., et al. (2013). Social genome: Putting big data to work for population informatics. Computer, 47(1), 56–63.CrossRef Kum, H. C., et al. (2013). Social genome: Putting big data to work for population informatics. Computer, 47(1), 56–63.CrossRef
5.
go back to reference 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. 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.
go back to reference Garcia-M, O., et al. (2013). Cooperative security in distributed networks. Computer Communications, 36, 1284–1297.CrossRef Garcia-M, O., et al. (2013). Cooperative security in distributed networks. Computer Communications, 36, 1284–1297.CrossRef
8.
go back to reference Li, Y., et al. (2017). Intelligent cryptography approach for secure distributed big data storage in cloud computing. Information Sciences, 387, 103–115.MATHCrossRef Li, Y., et al. (2017). Intelligent cryptography approach for secure distributed big data storage in cloud computing. Information Sciences, 387, 103–115.MATHCrossRef
9.
go back to reference 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.CrossRef 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.CrossRef
10.
go back to reference Amirhossein, F., et al. (2018). Middleware technologies for cloud of things-a survey. Digital Communications and Networks, 4(3), 1–13. Amirhossein, F., et al. (2018). Middleware technologies for cloud of things-a survey. Digital Communications and Networks, 4(3), 1–13.
11.
go back to reference 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.CrossRef 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.CrossRef
12.
go back to reference Alrawais, A., et al. (2017). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet Computing, 21(2), 34–42.CrossRef Alrawais, A., et al. (2017). Fog computing for the Internet of Things: Security and privacy issues. IEEE Internet Computing, 21(2), 34–42.CrossRef
13.
go back to reference 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 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.
go back to reference 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. 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.
15.
go back to reference 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.CrossRef 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.CrossRef
16.
go back to reference 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.CrossRef 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.CrossRef
17.
go back to reference 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.CrossRef 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.CrossRef
18.
go back to reference Fernandez-Gago, C., Moyano, F., & Lopez, J. (2017). Modelling trust dynamics in the internet of things. Information Sciences Journal, 396, 72–82.CrossRef Fernandez-Gago, C., Moyano, F., & Lopez, J. (2017). Modelling trust dynamics in the internet of things. Information Sciences Journal, 396, 72–82.CrossRef
19.
go back to reference Gai, K., & Qiu, M. (2018). Optimal resource allocation using reinforcement learning for IoT content-centric services. Applied Soft Computing, 70, 12–21.CrossRef Gai, K., & Qiu, M. (2018). Optimal resource allocation using reinforcement learning for IoT content-centric services. Applied Soft Computing, 70, 12–21.CrossRef
20.
go back to reference Vohra, K., & Dave, M. (2018). Multi-authority attribute based data access control in fog computing. Procedia Computer Science, 132, 1449–1457.CrossRef Vohra, K., & Dave, M. (2018). Multi-authority attribute based data access control in fog computing. Procedia Computer Science, 132, 1449–1457.CrossRef
21.
go back to reference Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17–39.CrossRef Čolaković, A., & Hadžialić, M. (2018). Internet of Things (IoT): A review of enabling technologies, challenges, and open research issues. Computer Networks, 144, 17–39.CrossRef
22.
go back to reference 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. 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.
go back to reference Lukas, M., et al. (2016). On perspective of security and privacy-preserving solutions in the internet of things. Computer Networks, 102, 83–95.CrossRef Lukas, M., et al. (2016). On perspective of security and privacy-preserving solutions in the internet of things. Computer Networks, 102, 83–95.CrossRef
24.
go back to reference 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.CrossRef 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.CrossRef
25.
go back to reference Varghese, B., et al. (2016). Challenges and opportunities in edge computing. In: 2016 IEEE international conference on smart cloud (SmartCloud) (pp. 20–26). IEEE 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.
go back to reference 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. 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.
go back to reference 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. 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.
go back to reference Sabrina, S., et al. (2017). Security towards the edge: Sticky policy enforcement for networked smart objects. Information Systems, 71, 78–89.CrossRef Sabrina, S., et al. (2017). Security towards the edge: Sticky policy enforcement for networked smart objects. Information Systems, 71, 78–89.CrossRef
29.
go back to reference 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.MATHCrossRef 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.MATHCrossRef
30.
go back to reference Hui, G., et al. (2018). A scalable and manageable IoT architecture based on transparent computing. Journal of Parallel and Distributed Computing, 118, 5–13.CrossRef Hui, G., et al. (2018). A scalable and manageable IoT architecture based on transparent computing. Journal of Parallel and Distributed Computing, 118, 5–13.CrossRef
31.
go back to reference Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, 27–38.CrossRef Verma, P., & Sood, S. K. (2018). Cloud-centric IoT based disease diagnosis healthcare framework. Journal of Parallel and Distributed Computing, 116, 27–38.CrossRef
32.
go back to reference 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). 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.
go back to reference Junchao, W., et al. (2018). A novel security scheme for Body Area Networks compatible with smart vehicles. Computer Networks, 143, 74–81.CrossRef Junchao, W., et al. (2018). A novel security scheme for Body Area Networks compatible with smart vehicles. Computer Networks, 143, 74–81.CrossRef
34.
go back to reference 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.CrossRef 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.CrossRef
35.
go back to reference 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.CrossRef 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.CrossRef
36.
go back to reference 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.CrossRef 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.CrossRef
37.
go back to reference 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.CrossRef 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.CrossRef
38.
go back to reference 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). 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.
go back to reference 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.CrossRef 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.CrossRef
40.
go back to reference 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. 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.
41.
go back to reference Eleni, K., et al. (2015). Elastic virtual machine placement in cloud computing network environments. Computer Networks, 93, 435–447.CrossRef Eleni, K., et al. (2015). Elastic virtual machine placement in cloud computing network environments. Computer Networks, 93, 435–447.CrossRef
42.
go back to reference Jalali, F. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34, 1728–1739.CrossRef Jalali, F. (2016). Fog computing may help to save energy in cloud computing. IEEE Journal on Selected Areas in Communications, 34, 1728–1739.CrossRef
43.
go back to reference Anwar, S., et al. (2017). From intrusion detection to an intrusion response system: Fundamentals, requirements, and future directions. MDPI Algorithms, 10(2), 1–24. Anwar, S., et al. (2017). From intrusion detection to an intrusion response system: Fundamentals, requirements, and future directions. MDPI Algorithms, 10(2), 1–24.
44.
go back to reference 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). 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.
go back to reference 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.CrossRef 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.CrossRef
46.
go back to reference Huang, Z. (2017). Insight of the protection for data security under selective opening attacks. Information Sciences, 412, 223–241.MATHCrossRef Huang, Z. (2017). Insight of the protection for data security under selective opening attacks. Information Sciences, 412, 223–241.MATHCrossRef
47.
go back to reference Li, J. (2018). Secure attribute-based data sharing for resource-limited users in cloud computing. Computers & Security, 72, 1–12.CrossRef Li, J. (2018). Secure attribute-based data sharing for resource-limited users in cloud computing. Computers & Security, 72, 1–12.CrossRef
48.
go back to reference 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.CrossRef 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.CrossRef
49.
go back to reference Ibrahim, M. H. (2016). Octopus: An edge-fog mutual authentication scheme. International Journal Network Security, 18(6), 1089–1101. Ibrahim, M. H. (2016). Octopus: An edge-fog mutual authentication scheme. International Journal Network Security, 18(6), 1089–1101.
50.
go back to reference Chen, M., & Leung, V. C. (2018). From cloud-based communications to cognition-based communications: A computing perspective. Computer Communications, 128, 74–79.CrossRef Chen, M., & Leung, V. C. (2018). From cloud-based communications to cognition-based communications: A computing perspective. Computer Communications, 128, 74–79.CrossRef
51.
go back to reference 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.CrossRef 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.CrossRef
52.
go back to reference 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.CrossRef 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.CrossRef
53.
go back to reference 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.CrossRef 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.CrossRef
54.
go back to reference Aazam, M., & Huh, E.-N. (2016). Fog computing: The cloud-IoT/IoE middleware paradigm. IEEE Potentials, 35(3), 40–44.CrossRef Aazam, M., & Huh, E.-N. (2016). Fog computing: The cloud-IoT/IoE middleware paradigm. IEEE Potentials, 35(3), 40–44.CrossRef
55.
go back to reference 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.CrossRef 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.CrossRef
56.
go back to reference 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.CrossRef 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.CrossRef
57.
go back to reference Lu, R., et al. (2017). A lightweight privacy-preserving data aggregation scheme for Fog computing-enhanced IoT. IEEE Access, 5, 3302–3312.CrossRef Lu, R., et al. (2017). A lightweight privacy-preserving data aggregation scheme for Fog computing-enhanced IoT. IEEE Access, 5, 3302–3312.CrossRef
58.
go back to reference 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). 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.
go back to reference Fei, H., He, J., & Wang, M. (2017). Research on fog computing based active anti-theft technology. Procedia Computer Science, 111, 209–213.CrossRef Fei, H., He, J., & Wang, M. (2017). Research on fog computing based active anti-theft technology. Procedia Computer Science, 111, 209–213.CrossRef
60.
go back to reference 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.CrossRef 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.CrossRef
61.
go back to reference Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things Journal, 3(6), 854–864.CrossRef Chiang, M., & Zhang, T. (2016). Fog and IoT: An overview of research opportunities. IEEE Internet of things Journal, 3(6), 854–864.CrossRef
62.
go back to reference He, Q., et al. (2017). Fog-based transcoding for crowd sourced video live cast. IEEE Communications Magazine, 55(4), 28–33.CrossRef He, Q., et al. (2017). Fog-based transcoding for crowd sourced video live cast. IEEE Communications Magazine, 55(4), 28–33.CrossRef
63.
go back to reference 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. 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.
go back to reference 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). 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.
go back to reference Fadele, A. A., et al. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10–28.CrossRef Fadele, A. A., et al. (2017). Internet of Things security: A survey. Journal of Network and Computer Applications, 88, 10–28.CrossRef
66.
go back to reference 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.CrossRef 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.CrossRef
67.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
69.
go back to reference 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.CrossRef 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.CrossRef
70.
go back to reference 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). 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.
go back to reference 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.CrossRef 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.CrossRef
72.
go back to reference Sun, P., et al. (2019). Modeling and clustering attacker activities in IoT through machine learning techniques. Information Sciences, 479, 456–471.CrossRef Sun, P., et al. (2019). Modeling and clustering attacker activities in IoT through machine learning techniques. Information Sciences, 479, 456–471.CrossRef
73.
go back to reference Yaqoob, I., et al. (2017). The rise of ransomware and emerging security challenges in the Internet of Things. Computer Networks, 129, 444–458.CrossRef Yaqoob, I., et al. (2017). The rise of ransomware and emerging security challenges in the Internet of Things. Computer Networks, 129, 444–458.CrossRef
74.
go back to reference 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.CrossRef 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.CrossRef
75.
go back to reference 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). 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.
go back to reference 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). 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.
go back to reference 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. 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.
go back to reference Ola, S., et al. (2018). IoT survey: An SDN and fog computing perspective. Computer Networks, 143, 221–246.CrossRef Ola, S., et al. (2018). IoT survey: An SDN and fog computing perspective. Computer Networks, 143, 221–246.CrossRef
79.
go back to reference Ziegeldorf, J. H., et al. (2016). Secure and anonymous decentralized Bitcoin mixing, Future Gener. Computing Systems, 80, 448–466. Ziegeldorf, J. H., et al. (2016). Secure and anonymous decentralized Bitcoin mixing, Future Gener. Computing Systems, 80, 448–466.
80.
go back to reference Singh, J., et al. (2016). Twenty security considerations for cloud supported Internet of Things. IEEE Internet of Things J., 3(3), 269–284.CrossRef Singh, J., et al. (2016). Twenty security considerations for cloud supported Internet of Things. IEEE Internet of Things J., 3(3), 269–284.CrossRef
81.
go back to reference 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). 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.
go back to reference Partha, P. R. (2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1, 35–46.CrossRef Partha, P. R. (2016). A survey of IoT cloud platforms. Future Computing and Informatics Journal, 1, 35–46.CrossRef
83.
go back to reference 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.CrossRef 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.CrossRef
84.
go back to reference 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.CrossRef 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.CrossRef
85.
go back to reference 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.CrossRef 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.CrossRef
86.
go back to reference 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.CrossRef 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.CrossRef
87.
go back to reference 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.CrossRef 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.CrossRef
88.
go back to reference Wang, T., et al. (2017). Trajectory privacy preservation based on a fog structure for Cloud location services. IEEE Access, 5, 7692–7701.CrossRef Wang, T., et al. (2017). Trajectory privacy preservation based on a fog structure for Cloud location services. IEEE Access, 5, 7692–7701.CrossRef
89.
go back to reference da Cristiano, C. A., et al. (2018). Internet of Health Things: Toward intelligent vital signs monitoring inhospitalwards. Artificial Intelligence in Medicine, 89, 61–69.CrossRef da Cristiano, C. A., et al. (2018). Internet of Health Things: Toward intelligent vital signs monitoring inhospitalwards. Artificial Intelligence in Medicine, 89, 61–69.CrossRef
90.
go back to reference Wang, K. H., et al. (2017). A secure authentication scheme for Internet of Things. Pervasive and Mobile Computing, 42, 15–26.CrossRef Wang, K. H., et al. (2017). A secure authentication scheme for Internet of Things. Pervasive and Mobile Computing, 42, 15–26.CrossRef
91.
go back to reference 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.CrossRef 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.CrossRef
92.
go back to reference Nesrine, K., & Maryline, L. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120–141.CrossRef Nesrine, K., & Maryline, L. (2017). Data security and privacy preservation in cloud storage environments based on cryptographic mechanisms. Computer Communications, 111, 120–141.CrossRef
93.
go back to reference 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). 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.
go back to reference 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). 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.
go back to reference 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.CrossRef 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.CrossRef
96.
go back to reference 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. 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.
97.
go back to reference 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.CrossRef 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.CrossRef
98.
go back to reference 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.CrossRef 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.CrossRef
99.
go back to reference 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.CrossRef 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.CrossRef
100.
go back to reference Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656.CrossRef Mach, P., & Becvar, Z. (2017). Mobile edge computing: A survey on architecture and computation offloading. IEEE Communications Surveys & Tutorials, 19(3), 1628–1656.CrossRef
101.
go back to reference 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.CrossRef 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.CrossRef
102.
go back to reference 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.CrossRef 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.CrossRef
103.
go back to reference 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. 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.
go back to reference 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.CrossRef 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.CrossRef
105.
go back to reference 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.CrossRef 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.CrossRef
106.
go back to reference 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.CrossRef 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.CrossRef
107.
go back to reference Nellore, K., & Hancke, G. P. (2016). A survey on urban traffic management system using wireless sensor networks. Sensors, 16(2), 157.CrossRef Nellore, K., & Hancke, G. P. (2016). A survey on urban traffic management system using wireless sensor networks. Sensors, 16(2), 157.CrossRef
108.
go back to reference 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. 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.
109.
go back to reference Feng, D., et al. (2013). Device-to-device communications underlaying cellular networks. IEEE Transactions on communications, 61(8), 3541–3551.CrossRef Feng, D., et al. (2013). Device-to-device communications underlaying cellular networks. IEEE Transactions on communications, 61(8), 3541–3551.CrossRef
110.
go back to reference Habibzadeh, H., et al. (2018). Sensing, communication and security planes: A new challenge for a smart city system design. Computer Networks, 144, 163–200.CrossRef Habibzadeh, H., et al. (2018). Sensing, communication and security planes: A new challenge for a smart city system design. Computer Networks, 144, 163–200.CrossRef
111.
go back to reference Min, C., et al. (2018). Cognitive internet of vehicles. Computer Communications, 120, 58–70.CrossRef Min, C., et al. (2018). Cognitive internet of vehicles. Computer Communications, 120, 58–70.CrossRef
112.
go back to reference 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). 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.
go back to reference Mora, H., et al. (2017). Distributed computational model for shared processing on Cyber-Physical System environments. Computer Communications, 111, 68–83.CrossRef Mora, H., et al. (2017). Distributed computational model for shared processing on Cyber-Physical System environments. Computer Communications, 111, 68–83.CrossRef
114.
go back to reference 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. Lee, J., Behrad, B., & Kao, H. A. (2015). A cyber-physical systems architecture for Industry 4.0-based manufacturing systems. Manufacturing Letters3, 18–23.
115.
go back to reference 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.CrossRef 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.CrossRef
116.
go back to reference Feng, J., et al. (2017). Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Transactions on Vehicular Technology, 66(12), 10660–10675.CrossRef Feng, J., et al. (2017). Autonomous vehicular edge computing framework with ACO-based scheduling. IEEE Transactions on Vehicular Technology, 66(12), 10660–10675.CrossRef
117.
go back to reference Ni, J., Zhang, A., & Shen, X. S. (2017). Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Communications Magazine, 55(6), 146–152.CrossRef Ni, J., Zhang, A., & Shen, X. S. (2017). Security, privacy, and fairness in fog-based vehicular crowdsensing. IEEE Communications Magazine, 55(6), 146–152.CrossRef
118.
go back to reference 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.CrossRef 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.CrossRef
119.
go back to reference 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. 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.
120.
go back to reference Chen, X., et al. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE Transactions on Networking, 24(5), 2795–2808.CrossRef Chen, X., et al. (2016). Efficient multi-user computation offloading for mobile-edge cloud computing. IEEE Transactions on Networking, 24(5), 2795–2808.CrossRef
121.
go back to reference Wang, Xu., et al. (2019). Survey on blockchain for Internet of Things. Computer Cummunication, 136, 10–29. Wang, Xu., et al. (2019). Survey on blockchain for Internet of Things. Computer Cummunication, 136, 10–29.
122.
go back to reference 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. 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.
123.
go back to reference 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.CrossRef 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.CrossRef
124.
go back to reference Sharma, A. K., Ashish, R., & Sharma, V. K. (2015). Biometric system- a review. IJCSIT, 6(5), 4616–4619. Sharma, A. K., Ashish, R., & Sharma, V. K. (2015). Biometric system- a review. IJCSIT, 6(5), 4616–4619.
125.
go back to reference Kalra, S., & Sood, S. K. (2015). Secure authentication scheme for IoT and cloud servers. Journal of Pervasive and Mobile Computing, 24, 210–223.CrossRef Kalra, S., & Sood, S. K. (2015). Secure authentication scheme for IoT and cloud servers. Journal of Pervasive and Mobile Computing, 24, 210–223.CrossRef
126.
go back to reference 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.CrossRef 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.CrossRef
127.
go back to reference 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). 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.
go back to reference Leonardo, M., et al. (2017). Optimized P2P streaming for wireless distributed networks. Pervasive and Mobile Computing, 42, 335–350.CrossRef Leonardo, M., et al. (2017). Optimized P2P streaming for wireless distributed networks. Pervasive and Mobile Computing, 42, 335–350.CrossRef
129.
go back to reference 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). 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.
go back to reference 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). 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.
go back to reference Motlagh, N. H., Bagaa, M., & Taleb, T. (2017). UAV-based IoT platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2), 128–134.CrossRef Motlagh, N. H., Bagaa, M., & Taleb, T. (2017). UAV-based IoT platform: A crowd surveillance use case. IEEE Communications Magazine, 55(2), 128–134.CrossRef
132.
go back to reference 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.CrossRef 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.CrossRef
133.
go back to reference 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.CrossRef 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.CrossRef
134.
go back to reference 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. 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.
135.
go back to reference Sunyaev, A. (2020). Cloud computing. Internet Computing (pp. 195–236). Springer.CrossRef Sunyaev, A. (2020). Cloud computing. Internet Computing (pp. 195–236). Springer.CrossRef
136.
go back to reference 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.CrossRef 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.CrossRef
137.
go back to reference Ahmed, W., et al. (2021). Security in next generation mobile payment systems: A comprehensive survey. IEEE Access, 9, 115932–115950.CrossRef Ahmed, W., et al. (2021). Security in next generation mobile payment systems: A comprehensive survey. IEEE Access, 9, 115932–115950.CrossRef
138.
go back to reference 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). 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.
go back to reference 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.CrossRef 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.CrossRef
140.
go back to reference Harkut, D. G. (2020). Introductory chapter cloud computing security challenges. Cloud computing security-concepts and practice. Intech Open.CrossRef Harkut, D. G. (2020). Introductory chapter cloud computing security challenges. Cloud computing security-concepts and practice. Intech Open.CrossRef
141.
go back to reference Basit, A., et al. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76, 139–154.CrossRef Basit, A., et al. (2021). A comprehensive survey of AI-enabled phishing attacks detection techniques. Telecommunication Systems, 76, 139–154.CrossRef
142.
go back to reference 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). 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.
go back to reference Pothuganti, S. (2020). Overview on security issues in cloud computing. International Journal of Innovation Research Computer Communication Engineering, 10, 4064–4068. Pothuganti, S. (2020). Overview on security issues in cloud computing. International Journal of Innovation Research Computer Communication Engineering, 10, 4064–4068.
144.
go back to reference 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. 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.
145.
go back to reference Logesswari, S., et al. (2020). A study on cloud computing challenges and its mitigations. Materials Today Proceedings, 1–5. Logesswari, S., et al. (2020). A study on cloud computing challenges and its mitigations. Materials Today Proceedings, 1–5.
146.
go back to reference 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). 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.
148.
go back to reference 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. 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.
149.
go back to reference Iwendi, C., et al. (2020). Keysplitwatermark: Zero watermarking algorithm for software protection against cyber-attacks. IEEE Access, 8, 72650–72660.CrossRef Iwendi, C., et al. (2020). Keysplitwatermark: Zero watermarking algorithm for software protection against cyber-attacks. IEEE Access, 8, 72650–72660.CrossRef
151.
go back to reference 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.CrossRef 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.CrossRef
152.
go back to reference 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. 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.
153.
go back to reference Namasudra, S., et al. (2020). Towards DNA based data security in the cloud computing environment. Computer Communications, 151, 539–547.CrossRef Namasudra, S., et al. (2020). Towards DNA based data security in the cloud computing environment. Computer Communications, 151, 539–547.CrossRef
155.
go back to reference Hina, M., et al. (2021). SeFACED: Semantic-based forensic analysis and classification of E-Mail data using deep learning. IEEE Access, 9, 98398–98411.CrossRef Hina, M., et al. (2021). SeFACED: Semantic-based forensic analysis and classification of E-Mail data using deep learning. IEEE Access, 9, 98398–98411.CrossRef
156.
go back to reference 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.CrossRef 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.CrossRef
157.
go back to reference 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.CrossRef 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.CrossRef
158.
go back to reference Shahzad, F., et al. (2021). Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. TechRxiv 2021. Shahzad, F., et al. (2021). Future smart cities: Requirements, emerging technologies, applications, challenges, and future aspects. TechRxiv 2021.
159.
go back to reference 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. 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.
161.
go back to reference 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. 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.
go back to reference Shabbir, M., et al. (2021). Enhancing security of health information using modular encryption standard in mobile cloud computing. IEEE Access, 9, 8820–8834.CrossRef Shabbir, M., et al. (2021). Enhancing security of health information using modular encryption standard in mobile cloud computing. IEEE Access, 9, 8820–8834.CrossRef
163.
go back to reference Al-Khafajiy, M., et al. (2020). COMITMENT: A fog computing trust management approach. Journal of Parallel and Distributed Computing, 137, 1–16.CrossRef Al-Khafajiy, M., et al. (2020). COMITMENT: A fog computing trust management approach. Journal of Parallel and Distributed Computing, 137, 1–16.CrossRef
164.
go back to reference 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.CrossRef 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.CrossRef
165.
go back to reference 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. 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.
166.
go back to reference Ahamad, R. Z., et al. (2021). Interference mitigation in D2D communication underlying cellular networks: Towards green energy. CMC-Computer Materials Continua, 68, 45–58.CrossRef Ahamad, R. Z., et al. (2021). Interference mitigation in D2D communication underlying cellular networks: Towards green energy. CMC-Computer Materials Continua, 68, 45–58.CrossRef
167.
go back to reference 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.CrossRef 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.CrossRef
168.
go back to reference Javed, A. R., et al. (2021). Green 5G: Enhancing capacity and coverage in device-to-device communication. Computer Materials Continua, 67, 1933–1950.CrossRef Javed, A. R., et al. (2021). Green 5G: Enhancing capacity and coverage in device-to-device communication. Computer Materials Continua, 67, 1933–1950.CrossRef
169.
go back to reference Singh, A. (2019). Security concerns and countermeasures in cloud computing: A qualitative analysis. International Journal of Information Technology, 11, 683–690.CrossRef Singh, A. (2019). Security concerns and countermeasures in cloud computing: A qualitative analysis. International Journal of Information Technology, 11, 683–690.CrossRef
170.
go back to reference 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.CrossRef 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.CrossRef
171.
go back to reference 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. 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.
Metadata
Title
Cloud and Big Data Security System’s Review Principles: A Decisive Investigation
Authors
KamtaNath Mishra
Vandana Bhattacharjee
Shashwat Saket
Shivam P. Mishra
Publication date
10-06-2022
Publisher
Springer US
Published in
Wireless Personal Communications / Issue 2/2022
Print ISSN: 0929-6212
Electronic ISSN: 1572-834X
DOI
https://doi.org/10.1007/s11277-022-09781-0

Other articles of this Issue 2/2022

Wireless Personal Communications 2/2022 Go to the issue