Securing the Skies With Advanced Anomaly Detection and Privacy Preservation in Cloud Computing Ecosystems

DOI

10.2991/978-94-6463-718-2_98How to use a DOI?

Keywords

cloud security; anomaly detection; privacy preservation; machine learning; Cloud Computing ecosystems

Abstract

The rapid adoption of cloud computing has brought forth a myriad of security challenges, necessitating robust and proactive measures to ensure data privacy and protection. This research explores the current landscape of security in the cloud, specifically anomaly detection methods, and cloud privacy preservation techniques. I perform a broad literature survey to find the best approaches to detecting and mitigating threats while maintaining user privacy. As a result, a behavior-based anomaly detection system is built, where advanced machine learning algorithms, like deep learning and unsupervised learning, are used for the detection, and potential threats to cloud ecosystem can be flagged. By monitoring the behavior of both the users and the system itself, this system can identify the patterns that indicate malicious behavior, such as data breaches and unauthorized access. In addition, proactive privacy-preserving mechanisms are strategically formulated and enforced to protect sensitive user data from exposure and malicious use. Techniques used in these approaches include data anonymization, differential privacy, and secure multi-party computation, which preserve the utility of user data for legitimate use cases while ensuring privacy and integrity. The solutions presented are aimed at better security in the cloud environment, making cloud computing services more reliable.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - P. Alagu Manoharan
AU  - M. Mohan
PY  - 2025
DA  - 2025/05/23
TI  - Securing the Skies With Advanced Anomaly Detection and Privacy Preservation in Cloud Computing Ecosystems
BT  - Proceedings of the International Conference on Sustainability Innovation in Computing and Engineering (ICSICE 2024)
PB  - Atlantis Press
SP  - 1173
EP  - 1191
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-718-2_98
DO  - 10.2991/978-94-6463-718-2_98
ID  - Manoharan2025
ER  -