Monitoring of Unauthorized Identity and Access Behaviour for Outsourced Data in Cloud Environment

Year : 2024 | Volume :14 | Issue : 02 | Page : –
By

Shivi Chauhan

Gopesh Singal

Himanshu Yadav

Yash Raj

Aditi Bhardwaj

  1. Student Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University Noida Uttar Pradesh India
  2. Student Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University Noida Uttar Pradesh India
  3. Student Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University Noida Uttar Pradesh India
  4. Student Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University Noida Uttar Pradesh India
  5. Associate Professor Department of Computer Science and Engineering, Amity School of Engineering & Technology, Amity University Noida Uttar Pradesh India

Abstract

The outsourcing of data is a significant challenge in the modern cloud computing ecosystem when it comes to tracking unauthorized identification and access behavior. In order to overcome this issue, this research suggests a thorough method for reliable anomaly detection in cloud systems. Improving data security and offering a trustworthy monitoring system are the two main goals. The suggested approach proceeds methodically, gathering information from several sources such as user profiles, cloud logs, and access records. Using state-of-the-art tools like AWS CloudTrail and Apache Kafka, the data is carefully preprocessed, cleaned, normal-ized, and feature extracted. The representation of user behaviour patterns is greatly aided by feature engineering, which takes into account variables such as system commands, file access patterns, and frequency of logins. Various strategies are em-ployed for anomaly identification, which include unsupervised learning algorithms (like k-means and isolation forest), statistical approaches, and neural networks (like autoencoders and RNNs). Proactive security measures are guaranteed via dash-boards, automated reaction mechanisms, and real-time warnings. Offering a com-prehensive security solution, the system smoothly connects with security incident and event management (SIEM) systems. Extensive testing results demonstrate the system’s effectiveness in detecting unauthorized access, providing security staff with important information. This study adds a sophisticated framework to strengthen cloud security and improves the conversation about identity theft and unauthorized access in outsourced data settings.

Keywords: cloud security, anomaly detection, unauthorized access, machine learning, data out-sourcing

[This article belongs to Journal of Communication Engineering & Systems(joces)]

How to cite this article: Shivi Chauhan, Gopesh Singal, Himanshu Yadav, Yash Raj, Aditi Bhardwaj. Monitoring of Unauthorized Identity and Access Behaviour for Outsourced Data in Cloud Environment. Journal of Communication Engineering & Systems. 2024; 14(02):-.
How to cite this URL: Shivi Chauhan, Gopesh Singal, Himanshu Yadav, Yash Raj, Aditi Bhardwaj. Monitoring of Unauthorized Identity and Access Behaviour for Outsourced Data in Cloud Environment. Journal of Communication Engineering & Systems. 2024; 14(02):-. Available from: https://journals.stmjournals.com/joces/article=2024/view=146807





References

  1. Tabrizchi H, Kuchaki Rafsanjani M. A survey on security challenges in cloud computing: issues, threats, and solutions. The journal of supercomputing. 2020 Dec;76(12):9493-532.
  2. Chandrasekaran K, Thomas MV. Distributed access control in cloud computing systems. Encyclopedia of Cloud Computing. 2016 Jun 9:417-32.
  3. Liu M, Xue Z, Xu X, Zhong C, Chen J. Host-based intrusion detection system with system calls: Review and future trends. ACM computing surveys (CSUR). 2018 Nov 19;51(5):1-36.
  4. Huang Q, Yang Y, Yue W, He Y. Secure data group sharing and conditional dissemination with multi-owner in cloud computing. IEEE Transactions on Cloud Computing. 2019 Mar 29;9(4):1607-18.
  5. Axelsson S. Intrusion detection systems: A survey and taxonomy. https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=7a15948bdcb530e2c1deedd8d22dd9b54788a634
  6. Pang G, Shen C, Cao L, Hengel AV. Deep learning for anomaly detection: A review. ACM computing surveys (CSUR). 2021 Mar 5;54(2):1-38.
  7. Khaliq S, Tariq ZU, Masood A. Role of user and entity behavior analytics in detecting insider attacks. In2020 International Conference on Cyber Warfare and Security (ICCWS) 2020 Oct 20 (pp. 1-6). IEEE.
  8. Singh M, Mehtre BM, Sangeetha S. User behavior profiling using ensemble approach for insider threat detection. In2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA) 2019 Jan 22 (pp. 1-8). IEEE.
  9. G. Martín A, Fernández-Isabel A, Martín de Diego I, Beltrán M. A survey for user behavior analysis based on machine learning techniques: current models and applications. Applied Intelligence. 2021 Aug;51(8):6029-55.
  10. Kim J, Park M, Kim H, Cho S, Kang P. Insider threat detection based on user behavior modeling and anomaly detection algorithms. Applied Sciences. 2019 Sep 25;9(19):4018.
  11. Ye Y, Li T, Adjeroh D, Iyengar SS. A survey on malware detection using data mining techniques. ACM Computing Surveys (CSUR). 2017 Jun 29;50(3):1-40.
  12. Tounsi W, Rais H. A survey on technical threat intelligence in the age of sophisticated cyber attacks. Computers & security. 2018 Jan 1;72:212-33.
  13. Jain R, Bhatnagar R. Applications of machine learning in cyber security-A review and a conceptual framework for a university setup. InThe International Conference on Advanced Machine Learning Technologies and Applications (AMLTA2019) 4 2020 (pp. 599-608). Springer International Publishing.
  14. Chen Z, Cao Y, Liu Y, Wang H, Xie T, Liu X. A comprehensive study on challenges in deploying deep learning based software. InProceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering 2020 Nov 8 (pp. 750-762).
  15. Nassif AB, Talib MA, Nasir Q, Dakalbab FM. Machine learning for anomaly detection: A systematic review. Ieee Access. 2021 May 24;9:78658-700.
  16. Rahmani AM, Azhir E, Ali S, Mohammadi M, Ahmed OH, Ghafour MY, Ahmed SH, Hosseinzadeh M. Artificial intelligence approaches and mechanisms for big data analytics: a systematic study. PeerJ Computer Science. 2021 Apr 14;7:e488.
  17. Nayak R, Pati UC, Das SK. A comprehensive review on deep learning-based methods for video anomaly detection. Image and Vision Computing. 2021 Feb 1;106:104078.
  18. Habib G, Sharma S, Ibrahim S, Ahmad I, Qureshi S, Ishfaq M. Blockchain technology: benefits, challenges, applications, and integration of blockchain technology with cloud computing. Future Internet. 2022 Nov 21;14(11):341.
  19. Chalapathy R, Chawla S. Deep learning for anomaly detection: A survey. arXiv preprint arXiv:1901.03407. 2019 Jan 10.
  20. Brown R, Lee RM. The evolution of cyber threat intelligence (cti): 2019 sans cti survey. SANS Institute. Available online: https://www. sans. org/white-papers/38790/(accessed on 12 July 2021). 2019 Feb.
  21. Ali RF, Shehzadi A, Jahankhani H, Hassan B. Emerging Trends in Cloud Computing Paradigm: An Extensive Literature Review on Cloud Security, Service Models, and Practical Suggestions. Cybersecurity and Artificial Intelligence: Transformational Strategies and Disruptive Innovation. 2024 Apr 18:117-42.
  22. Hamdan S, Ayyash M, Almajali S. Edge-computing architectures for internet of things applications: A survey. Sensors. 2020 Nov 11;20(22):6441.

Regular Issue Subscription Review Article
Volume 14
Issue 02
Received April 16, 2024
Accepted April 27, 2024
Published May 20, 2024