Comparative Analysis of Weighted Ensemble and Majority Voting Algorithms for Intrusion Detection in OpenStack Cloud Environments

In the ever-evolving landscape of cybersecurity, the detection of malicious activities within cloud environments remains a critical challenge. This research aims to compare the effectiveness of two ensemble algorithms, the weighted ensemble algorithm and the majority voting algorithm, in the context of intrusion detection within an OpenStack cloud environment. To conduct this study, a dataset was generated using a network of 10 virtual machines, simulating the complex dynamics of a real cloud infrastructure. Various attack scenarios were simulated, and system metrics including CPU usage, memory utilization, and network traffic were monitored and logged. The weighted ensemble algorithm combines the predictions of multiple individual models with varying weights, while the majority voting algorithm aggregates predictions from multiple models. Through a rigorous experimental setup, these algorithms were applied to the generated dataset, and their performance was evaluated using standard metrics such as accuracy, precision, recall, and F1-score. These findings provide valuable insights into the strengths and weaknesses of ensemble algorithms for intrusion detection in cloud environments. It highlights the importance of selecting appropriate algorithms based on specific security requirements and threat profiles. Different attack scenarios may require different algorithmic approaches to achieve optimal results. Overall, this study contributes to the understanding of ensemble techniques in cloud security and offers a foundation for further research in optimizing intrusion detection strategies within dynamic and complex cloud environments. By identifying the strengths and weaknesses of different ensemble algorithms, cybersecurity professionals can make informed decisions in selecting the most suitable approach to enhance the security of cloud environments.

@article{Patil2023,
  title     = {Comparative Analysis of Weighted Ensemble and Majority Voting Algorithms for Intrusion Detection in OpenStack Cloud Environments},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Pravin Patil and Geetanjali Kale and Nidhi Bivalkar and Agneya Kolhatkar},
  doi       = {10.14569/IJACSA.2023.0141276},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141276}
}