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A secure framework for the cloud to protect the virtual machine from malicious events

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Abstract

In recent years, Virtual Machine (VM) has played an important role for cloud computing applications to execute the assigned user tasks. However, the cloud is a big open environment, so it is vulnerable to attack; happening attack in VM can disturb the entire data sharing process. So the current research has proposed a novel Whale-based CatBoost (WbCB) mechanism to protect VM from malicious activities and to afford security for the connected VM’s in cloud computing. The main aim of this present article is to enhance the confidential score of the VM cloud sharing model. The required number of VM is primarily designed in the MATLAB environment, then a novel WbCB model was designed with suitable parameters to secure the data during transmission. The confidential score of the proposed model in front of the Cloud Malware Injection (CMI) attack has been investigated. Still, the proposed WbCB has reported the best confidential rate as 99%, data sharing rate as 0.965, and attack detection score as 99.8%. Also, the time taken for scheduling 6000 jobs is 1000s, and the reported lower false rate is 5%. Thus compared to the recent existing models, the proposed WbCB model has tremendously maximized the performance rate.

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References

  1. Ali A, Khan A, Ahmed M, Jeon G (2021) BCALS: Blockchain-based secure log management system for cloud computing Trans Emerg Telecommun Technol e4272. https://doi.org/10.1002/ett.4272

  2. Aljumah A, Ahanger TA (2020) Cyber security threats, challenges and defence mechanisms in cloud computing. IET Commun 14(7):1185–1191

    Article  Google Scholar 

  3. Alkadi O, Moustafa N, Turnbull B (2020) A review of intrusion detection and blockchain applications in the cloud: approaches, challenges and solutions. IEEE Access 8:104893–104917. https://doi.org/10.1109/ACCESS.2020.2999715

    Article  Google Scholar 

  4. Alkadi O, Moustafa N, Turnbull B, Choo KR (2020) A deep blockchain framework-enabled collaborative intrusion detection for protecting IoT and cloud networks. IEEE Internet Things J 8(12):9463–9472. https://doi.org/10.1109/JIOT.2020.2996590

    Article  Google Scholar 

  5. Alouffi B, Hasnain M, Alharbi A, Alosaimi W, Alyami H, Ayaz M (2021) A systematic literature review on cloud computing security: threats and mitigation strategies. IEEE Access 9:57792–57807. https://doi.org/10.1109/ACCESS.2021.3073203

    Article  Google Scholar 

  6. Alshehri M, Panda B (2020) Minimizing data breach by a malicious fog node within a fog federation. 2020 7th IEEE international conference on cyber security and cloud computing (CSCloud)/2020 6th IEEE international conference on edge computing and scalable cloud (EdgeCom), IEEE https://doi.org/10.1109/CSCloud-EdgeCom49738.2020.00016

  7. Aluvalu R, Chennam KK, Maheswari VU, Jabbar MA (2021) A novel and secure approach for quantum key distribution in a cloud computing environment. Intelligent Computing and Networking. Springer, Singapore, pp 271–283. https://doi.org/10.1007/978-981-15-7421-4_25

    Book  Google Scholar 

  8. Arora G, Bibhu V, Kohli R, Pavani P (2016) Multimodal biometrics for improvised security. In: 2016 international conference on innovation and challenges in cyber security (ICICCS-INBUSH), pp. 1-5. https://doi.org/10.1109/ICICCS.2016.7542312

  9. Baker T, Mackay M, Shaheed A et al (2015) Security-oriented cloud platform for soa-based scada. 2015 15th IEEE/ACM international symposium on cluster, Cloud Grid Computing IEEE https://doi.org/10.1109/CCGrid.2015.37

  10. Bhushan K, Gupta BB (2019) Network flow analysis for detection and mitigation of fraudulent resource consumption (FRC) attacks in multimedia cloud computing. Multimed Tools Appl 78(4):4267–4298. https://doi.org/10.1007/s11042-017-5522-z

    Article  Google Scholar 

  11. Chen Y, Wang F, Ma Y, Yao Y (2019) A distributed framework for solving and benchmarking security constrained unit commitment with warm start. IEEE Trans Power Syst 35(1):711–720. https://doi.org/10.1109/TPWRS.2019.2930706

    Article  Google Scholar 

  12. Chen CL, Huang PT, Deng YY, Chen HC, Wang YC (2020) A secure electronic medical record authorization system for smart device application in cloud computing environments. Hum Cent Comput Inf Sci 10(1):1–31. https://doi.org/10.1186/s13673-020-00221-1

    Article  Google Scholar 

  13. Chennam KK, Aluvalu R, Shitharth S (2021) An authentication model with high security for cloud database. Architectural Wireless Networks Solutions and Security Issues. Springer, Singapore, p 13. https://doi.org/10.1007/978-981-16-0386-0

    Book  Google Scholar 

  14. Choe H, Aurelia S (2020) HCI authentication to prevent internal threats in cloud computing. In: Convergence of ICT and Smart Devices for Emerging Applications. Springer, Cham, pp 119–135. https://doi.org/10.1007/978-3-030-41368-2_6

    Chapter  Google Scholar 

  15. Devi TJBD, Subramani A, Anitha P (2021) Modified adaptive neuro fuzzy inference system based load balancing for virtual machine with security in cloud computing environment. J Ambient Intell Humaniz Comput 12(3):3869–3876. https://doi.org/10.1007/s12652-020-01728-2

    Article  Google Scholar 

  16. Gerard A, Latif R, Iqbal W, Gerard N., Husnain Johar A, Asghar U (2020) Detection and prevention of a malicious activity in industrial federated cloud computing paradigm. International Conference on Applied Human Factors and Ergonomics, Springer, Cham https://doi.org/10.1007/978-3-030-51041-1_52

  17. Gonçalves CF (2017) Benchmarking the Security of Virtualization Infrastructures: Motivation and Approach. 2017 IEEE international symposium on software reliability engineering workshops (ISSREW), IEEE https://doi.org/10.1109/ISSREW.2017.70

  18. Gonçalves CF, Antunes N (2020) Vulnerability Analysis as Trustworthiness Evidence in Security Benchmarking: A Case Study on Xen. 2020 IEEE international symposium on software reliability engineering workshops (ISSREW), IEEE https://doi.org/10.1109/ISSREW51248.2020.00078

  19. Gupta BB, Yamaguchi S, Agrawal DP (2017) Editorial note: security and privacy of multimedia big data in Mobile and cloud computing. Multimed Tools Appl 76(21):22669–22669. https://doi.org/10.1007/s11042-017-5110-2

    Article  Google Scholar 

  20. Gupta BB, Yamaguchi S, Agrawal DP (2018) Advances in security and privacy of multimedia big data in mobile and cloud computing. Multimed Tools Appl 77(7):9203–9208. https://doi.org/10.1007/s11042-017-5301-x

    Article  Google Scholar 

  21. Kohli R, Garg A, Phutela S, Kumar Y, Jain S (2021) An improvised model for securing cloud-based e-healthcare systems, IoT in Health- care and Ambient Assisted Living, Springer, pp. 293–310. https://doi.org/10.1007/978-981-15-9897-5_14

  22. Kumar AMS, Venkatesan M (2019) Task scheduling in a cloud computing environment using HGPSO algorithm. Clust Comput 22(1):2179–2185. https://doi.org/10.1007/s10586-018-2515-2

    Article  Google Scholar 

  23. Masood A, Lakew DS, Cho S (2020) Security and privacy challenges in connected vehicular cloud computing. IEEE Commun Surv Tutor 22(4):2725–2764. https://doi.org/10.1109/COMST.2020.3012961

    Article  Google Scholar 

  24. Moustafa N, Creech G, Sitnikova E, Keshk M (2017) Collaborative anomaly detection framework for handling big data of cloud computing. 2017 military communications and information systems conference (MilCIS), IEEE https://doi.org/10.1109/MilCIS.2017.8190421

  25. Namasudra S, Devi D, Kadry S, Sundarasekar R, Shanthini A (2020) Towards DNA based data security in the cloud computing environment. Comput Commun 151:539–547. https://doi.org/10.1016/j.comcom.2019.12.041

    Article  Google Scholar 

  26. Negi PS, Garg A, Lal R (2020) Intrusion detection and prevention using honeypot network for cloud security. 2020 10th international conference on cloud computing, Data Sci Eng (confluence), IEEE. https://doi.org/10.1109/Confluence47617.2020.9057961

  27. Pallavi GB, Jayarekha P (2020) Secure and efficient multi-tenant database management system for cloud computing environment. Int J Inf Tecnol 1–9. https://doi.org/10.1007/s41870-019-00416-5

  28. Patil R, Dudeja H, Modi C (2019) Designing an efficient security framework for detecting intrusions in virtual network of cloud computing. Comput Secur 85:402–422. https://doi.org/10.1016/j.cose.2019.05.016

    Article  Google Scholar 

  29. Praveena D, Rangarajan P (2020) A machine learning application for reducing the security risks in hybrid cloud networks. Multimed Tools Appl 79(7):5161–5173. https://doi.org/10.1007/s11042-018-6339-0

    Article  Google Scholar 

  30. Qureshi KN, Jeon G, Piccialli F (2021) Anomaly detection and trust authority in artificial intelligence and cloud computing. Comput Netw 184:107647. https://doi.org/10.1016/j.comnet.2020.107647

    Article  Google Scholar 

  31. Rabbani M, Wang YL, Khoshkangini R, Jelodar H, Zhao R, Hu P (2020) A hybrid machine learning approach for malicious behaviour detection and recognition in cloud computing. J Netw Comput Appl 151:102507. https://doi.org/10.1016/j.jnca.2019.102507

    Article  Google Scholar 

  32. Rawas S (2021) Energy, network, and application-aware virtual machine placement model in SDN-enabled large scale cloud data centers. Multimed Tools Appl 80(10):15541–15562. https://doi.org/10.1007/s11042-021-10616-6

    Article  Google Scholar 

  33. Sun PJ (2020) Security and privacy protection in cloud computing: discussions and challenges. J Netw Comput Appl 160:102642. https://doi.org/10.1016/j.jnca.2020.102642

    Article  Google Scholar 

  34. Thanka MR, Maheswari PU, Edwin EB (2019) An improved efficient: artificial bee Colony algorithm for security and QoS aware scheduling in cloud computing environment. Clust Comput 22(5):10905–10913. https://doi.org/10.1007/s10586-017-1223-7

    Article  Google Scholar 

  35. Wang H, Wang Y, Taleb T, Jiang X (2020) Special issue on security and privacy in network computing. World Wide Web 23(2):951–957. https://doi.org/10.1007/s11280-019-00704-x

    Article  Google Scholar 

  36. Wu Y, Wang X, Susilo W, Yang G et al (2020) Efficient server-aided secure two-party computation in heterogeneous mobile cloud computing. IEEE Trans Dependable Secure Comput https://doi.org/10.1109/TDSC.2020.2966632

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  1. VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, 500090, India

    P. Neelakantan

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Neelakantan, P. A secure framework for the cloud to protect the virtual machine from malicious events. Multimed Tools Appl 82, 33811–33834 (2023). https://doi.org/10.1007/s11042-023-14740-3

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