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

Year : 2024 | Volume : 14 | Issue : 02 | Page : 9 19
    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 behaviour. 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, normalized, 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 employed 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 dashboards, automated reaction mechanisms, and real-time warnings. Offering a comprehensive 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 outsourcing

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

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):9-19.
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):9-19. Available from: https://journals.stmjournals.com/joces/article=2024/view=146807


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Regular Issue Subscription Review Article
Volume 14
Issue 02
Received 16/04/2024
Accepted 27/04/2024
Published 20/05/2024
412

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