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International Journal of Advanced Computer Science and Applications(IJACSA), Volume 15 Issue 12, 2024.
Abstract: Intrusion Detection Models (IDM) often suffer from poor accuracy, especially when facing coordinated attacks such as Distributed Denial of Service (DDoS). One significant limitation of existing IDM solutions is the lack of an effective technique to determine the optimal period for sharing attack information among nodes in a distributed IDM environment. This article pro-poses a novel collaborative IDM model that addresses this issue by leveraging the Pruned Exact Linear Time (PELT) change point detection algorithm. The PELT algorithm dynamically determines the appropriate intervals for disseminating attack information to nodes within the collaborative IDM framework. Additionally, to enhance detection accuracy, the proposed model integrates a Gradient Boosting Machine with a Support Vector Machine (GBM-SVM) for collaborative detection of malicious activities. The proposed model was implemented in Apache Spark using the NSL-KDD benchmark intrusion detection dataset. Experimental results demonstrate that this collaborative approach significantly improves detection accuracy and responsiveness to coordinated attacks, providing a robust solution for enhancing cloud security.
Widad Elbakri, Maheyzah Md. Siraj, Bander Ali Saleh Al-rimy, Sultan Ahmed Almalki, Tami Alghamdi, Azan Hamad Alkhorem and Frederick T. Sheldon, “Novel Collaborative Intrusion Detection for Enhancing Cloud Security” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151294
@article{Elbakri2024,
title = {Novel Collaborative Intrusion Detection for Enhancing Cloud Security},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151294},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151294},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Widad Elbakri and Maheyzah Md. Siraj and Bander Ali Saleh Al-rimy and Sultan Ahmed Almalki and Tami Alghamdi and Azan Hamad Alkhorem and Frederick T. Sheldon}
}
Copyright Statement: This is an open access article licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, even commercially as long as the original work is properly cited.