A Comprehensive Review of Information Assurance in Cloud Computing Environments

Authors

  • Nahid Neoaz Wilmington University, USA

Keywords:

I governing IAs, CC, DP, RM, Compliance, Security, CIA, C–Cloud, TL & DP.

Abstract

IA is information that relates to the safety or insecurity of data and service that reside in the cloud computing spaces. Due to the benefits that are enshrined by cloud technologies organizations are using the cloud technologies as solutions to scalability, costs and flexibility while addressing issues of data privacy and security threats and compliance. It is for this reason that in this paper, the following aspects will be addressed; information assurance and cloud computing and other elements of information assurance such as CIA triad, authentication and non-repudiation. It also defines risk at the present time to mean data breach, insider threats, and Distributed Denial of Service (DDoS) attack among others; and how each of these organizations can pursue risk management strategies to such risks. In addition, this paper examines the data privacy aspect and the legal regulation of and concerns with cloud security in the shared responsibility model. By going through the organization information assurance plan, it is asserted that risk identification, assessment and monitoring, and information asset protection such us data encryption, and access should be acknowledged. Thus it becomes possible to implant sound security solution into the cloud computing solutions/ So by emulating on the best practices in support of the security/organizations gain all the benefits of Cloud computing. This paper gives a detailed insight of how Information assurance in cloud environment in a world that is going digital can be handled.

References

Radwan T, Azer MA, Abdelbaki N. Cloud computing security: challenges and future trends. Int J Comput Appl Technol. 2017; 55(2):158–72. Doi: 10.1504/IJCAT. 2017.082865

Sharma P, Jindal R, Borah MD. Blockchain technology for cloud storage: a systematic literature review. ACM Comput Surv. 2020; 53(4):: 89:1–32. Doi: 10.1145/ 3403954

Alkadi O, Moustafa N, Turnbull B. A review of intrusion detection and blockchain applications in the cloud: approaches, challenges and solutions. IEEE Access. 2020; 8:104893–917. doi:10.1109/ACCESS.2020. 2999715.

Patel P, Patel H. Review of blockchain technology to address various security issues in cloud computing. In: Kotecha K, Piuri V, Shah H Patel R, editors. Data science and intelligent applications. Singapore: Springer; 2021. p. 345–54. Lecture Notes on Data Engineering and Communications Technologies.

Xie S, Zheng Z, Chen W, Wu J, Dai HN, Imran M. Blockchain for cloud exchange: a survey. Comput Electr Eng. 2020; 81:106526. doi:10.1016/j.compeleceng.2019. 106526

Gai K, Guo J, Zhu L, Yu S. Block chain meets cloud computing: a survey. IEEE Commun Surv Tut. 2020; 22 (3):2009–30. doi:10.1109/COMST.2020.2989392.

Pavithra S, Ramya S, Prathibha S. A survey on cloud security issues and blockchain. In: 2019 3rd International Conference on Computing and Communications Technologies (ICCCT); 2019 Feb. p. 136–40.

Memon R, Li J, Ahmed J, Nazeer I, Mangrio MI, Ali K. Cloud-based vs. Blockchain-based IoT: a comparative survey and way forward. Front Inform Technol Electron Eng. 2020; 21(4):563–86. Doi: 10.1631/FITEE. 1800343.

Murthy CB, Shri ML. A survey on integrating cloud computing with Blockchain. In: 2020 International Conference on Emerging Trends in Information Technology and Engineering (Ic-ETITE); 2020 Feb. p. 1–6.

Xu H, Cao J, Zhang J, Gong L, Gu Z. A survey: cloud data security based on blockchain technology. In: 2019 IEEE Fourth International Conference on Data Science in Cyberspace (DSC); 2019 Jun. p. 618–24.

Prianga S, Sagana R, Sharon E. Evolutionary survey on data security in cloud computing using blockchain. In: 2018 IEEE International Conference on System, Computation, Automation and Networking (ICSCA); 2018 Jul. p. 1–6.

Mohammadian V, Navimipour NJ, Hosseinzadeh M, Darwesh A. Comprehensive and systematic study on the fault tolerance architectures in cloud computing. J Circuits Syst Comput. 2020; 29(15):2050240. Doi: 10. 1142/S0218126620502400.

Isharufe W, Jaafar F, Butakov S. Study of security issues in platform-as-a-service (paas) cloud model. In: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE). IEEE; 2020. p. 1–6.

Shyam GK, Theja RSS. A survey on resolving security issues in SaaS through software defined networks. Int J Grid Util Comput. 2021; 12(1):1–14. Doi: 10.1504/ IJGUC.2021.112475.

R. Bonifazi, J. Vandenplas, J. ten Napel, K. Matilainen, R. F. Veerkamp, and M. P. L. Calus, “Impact of sub-setting the data of the main Limousin beef cattle population on the estimates of acrosscountry genetic correlations,” Genet. Sel. Evol., vol. 52, pp. 1–16, 2020.

D. A. Van Dyk and X.-L. Meng, “The art of data augmentation,” J. Comput. Graph. Stat., vol. 10, no. 1, pp. 1–50, 2001.

K. Houkjær, K. Torp, and R. Wind, “Simple and realistic data generation,” in Proceedings of the 32nd international conference on Very large data bases, 2006, pp. 1243–1246.

D. Evans, “Systematic reviews of interpretive research: interpretive data synthesis of processed data,” Aust. J. Adv. Nursing, vol. 20, no. 2, 2002

Z. Zhang, “Missing data imputation: focusing on single imputation,” Ann. Transl. Med., vol. 4, no. 1, 2016.

K. Weiss, T. M. Khoshgoftaar, and D. Wang, “A survey of transfer learning,” J. Big data, vol. 3, no. 1, pp. 1–40, 2016.

M. A. Bouke, A. Abdullah, J. Frnda, K. Cengiz, and B. Salah, “BukaGini: A Stability-Aware Gini Index Feature Selection Algorithm for Robust Model Performance,” IEEE Access, vol. 11, pp. 59386–59396, 2023, doi: 10.1109/ACCESS.2023.3284975

M. Sarhan, S. Layeghy, N. Moustafa, M. Gallagher, and M. Portmann, “Feature extraction for machine learning-based intrusion detection in IoT networks,” Digit. Commun. Networks, 2022, doi: 10.1016/j.dcan.2022.08.012.

M. A. Bouke, A. Abdullah, S. H. ALshatebi, M. T. Abdullah, and H. El Atigh, “An intelligent DDoS attack detection tree-based model using Gini index feature selection method,” Microprocess. Microsyst., vol. 98, no. March, p. 104823, 2023, doi: 10.1016/j.micpro.2023.104823.

A. J. Izenman, “Introduction to manifold learning,” Wiley Interdiscip. Rev. Comput. Stat., vol. 4, no. 5, pp. 439–446, 2012.

P. B. Udas, M. E. Karim, and K. S. Roy, “SPIDER: A shallow PCA based network intrusion detection system with enhanced recurrent neural networks,” J. King Saud Univ. - Comput. Inf. Sci., vol. 34, no. 10, pp. 10246–10272, 2022, doi: 10.1016/j.jksuci.2022.10.019.

Khan, M. I., Arif, A., & Khan, A. R. A. (2024). The Most Recent Advances and Uses of AI in Cybersecurity. BULLET: Jurnal Multidisiplin Ilmu, 3(4), 566-578.

Y. Himeur, K. Ghanem, A. Alsalemi, F. Bensaali, and A. Amira, “Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives,” Appl. Energy, vol. 287, no. November 2020, p. 116601, 2021, doi: 10.1016/j.apenergy.2021.116601.

S. Rao, A. K. Verma, and T. Bhatia, “A review on social spam detection: Challenges, open issues, and future directions,” Expert Syst. Appl., vol. 186, no. August, p. 115742, 2021, doi: 10.1016/j.eswa.2021.115742.

R. K. Deka, D. K. Bhattacharyya, and J. K. Kalita, “Active learning to detect DDoS attack using ranked features,” Comput. Commun., vol. 145, no. June, pp. 203–222, 2019, doi: 10.1016/j.comcom.2019.06.010.

T. Hamed, R. Dara, and S. C. Kremer, “Network intrusion detection system based on recursive feature addition and bigram technique,” Comput. Secur., vol. 73, pp. 137–155, 2018, doi: 10.1016/j.cose.2017.10.011

N. Pilnenskiy and I. Smetannikov, “Modern Implementations of Feature Selection Algorithms and Their Perspectives,” Conf. Open Innov. Assoc. Fruct, pp. 250–256, 2019, doi: 10.23919/FRUCT48121.2019.8981498.

B. Subba, S. Biswas, and S. Karmakar, “Enhancing performance of anomaly based intrusion detection systems through dimensionality reduction using principal component analysis,” in 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS), 2016, pp. 1–6

D. C. Can, H. Q. Le, and Q. T. Ha, “Detection of Distributed Denial of Service Attacks Using Automatic Feature Selection with Enhancement for Imbalance Dataset,” in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2021, pp. 386–398. doi: 10.1007/978-3-030-73280-6_31.

M. Di Mauro, G. Galatro, G. Fortino, and A. Liotta, “Supervised feature selection techniques in network intrusion detection: A critical review,” Eng. Appl. Artif. Intell. vol. 101, no. October 2020, p. 104216, 2021, doi: 10.1016/j.engappai.2021.104216.

I. H. Hassan, M. Abdullahi, M. M. Aliyu, S. A. Yusuf, and A. Abdulrahim, “An improved binary manta ray foraging optimization algorithm based feature selection and random forest classifier for network intrusion detection,” Intell. Syst. with Appl., vol. 16, no. November 2021, p. 200114, 2022, doi: 10.1016/j.iswa.2022.200114.

L. D’hooge, M. Verkerken, T. Wauters, B. Volckaert, and F. De Turck, “Hierarchical feature block ranking for data-efficient intrusion detection modeling,” Comput. Networks, vol. 201, no. February, p. 108613, 2021, doi: 10.1016/j.comnet.2021.108613.

E. Mushtaq, A. Zameer, and A. Khan, “A two-stage stacked ensemble intrusion detection system using five base classifiers and MLP with optimal feature selection,” Microprocess. Microsyst., vol. 94, no. December 2021, p. 104660, 2022, doi: 10.1016/j.micpro.2022.104660.

Z. Halim et al., “An effective genetic algorithm-based feature selection method for intrusion detection systems,” Comput. Secur. vol. 110, p. 102448, 2021, doi: 10.1016/j.cose.2021.102448.

Khan, M. I., Arif, A., & Khan, A. R. A. (2024). AI-Driven Threat Detection: A Brief Overview of AI Techniques in Cybersecurity. BIN: Bulletin of Informatics, 2(2), 248-261.

I. F. Kilincer, T. Tuncer, F. Ertam, and A. Sengur, “SPA-IDS: An intelligent intrusion detection system based on vertical mode decomposition and iterative feature selection in computer networks,” Microprocess. Microsyst. vol. 96, no. December 2021, p. 104752, 2023, doi: 10.1016/j.micpro.2022.104752.

R. Panigrahi et al., “Intrusion detection in cyber–physical environment using hybrid Naïve Bayes—Decision table and multiobjective evolutionary feature selection,” Comput. Commun. vol. 188, no. September 2021, pp. 133–144, 2022, doi: 10.1016/j.comcom.2022.03.009.

M. Artur, “Review the performance of the Bernoulli Naïve Bayes Classifier in Intrusion Detection Systems using Recursive Feature Elimination with Cross-validated selection of the best number of features,” Procedia Comput. Sci., vol. 190, no. 2019, pp. 564–570, 2021, doi: 10.1016/j.procs.2021.06.066.

V. Herrera-Semenets, L. Bustio-Martínez, R. Hernández-León, and J. van den Berg, “A multi-measure feature selection algorithm for efficacious intrusion detection,” Knowledge-Based Syst., vol. 227, p. 107264, 2021, doi: 10.1016/j.knosys.2021.107264.

S. M. Kasongo and Y. Sun, “Performance Analysis of Intrusion Detection Systems Using a Feature Selection Method on the UNSWNB15 Dataset,” J. Big Data, vol. 7, no. 1, 2020, doi: 10.1186/s40537- 020-00379-6.

R. A. Disha and S. Waheed, “Performance analysis of machine learning models for intrusion detection system using Gini Impuritybased Weighted Random Forest (GIWRF) feature selection technique,” Cybersecurity, vol. 5, no. 1, pp. 1–22, 2022, doi: 10.1186/s42400-021-00103-8.

D. Kshirsagar and S. Kumar, “Towards an intrusion detection system for detecting web attacks based on an ensemble of filter feature selection techniques,” Cyber-Physical Syst., vol. 00, no. 00, pp. 1–16, 2022, doi: 10.1080/23335777.2021.2023651.

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Published

2024-12-25

How to Cite

Neoaz, N. (2024). A Comprehensive Review of Information Assurance in Cloud Computing Environments. BULLET : Jurnal Multidisiplin Ilmu, 3(6), 715–725. Retrieved from https://journal.mediapublikasi.id/index.php/bullet/article/view/4898