Detection of Intruder in Cloud Computing Environment using Swarm Inspired based Neural Network

Cloud computing services offered a resource pool with a wide range of storage for large amounts of data. Cloud services are generally used as a demand-driven private or open data forum, and the increase in use has led to security concerns. Therefore, it is necessary to design an accurate Intrusion Detection System (IDS) to identify the suspected node in the cloud computing environment. This is possible by monitoring network traffic so that the quality of service and performance of the system can be maintained. Several researchers have worked on designing valid IDS with the help of a machine learning approach. A single classification algorithm seems to be impossible to detect intruders with high accuracy. Therefore, a hybrid approach is presented. This approach is a combination of Cuckoo Search. CS as an optimization algorithm and Feed Forward Back Propagation Neural Network (FFBPNN) as a multi-class classification approach. The user's request to access cloud data is collected and essential features are selected using CS as an optimization approach. The selected features are used to train FFBPNN with reduced training time and complexity. The experimental analysis has been performed in terms of precision, recall, F-measure, and accuracy. The evaluated value for parameters i.e., precision (85.5%), recall (86.4%), F-measure (85.9%), and accuracy (86.22%) are observed. At last, the parameters are also compared with the existing approach.

@article{Nishika2021,
  title     = {Detection of Intruder in Cloud Computing Environment using Swarm Inspired based Neural Network},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {12},
  number    = {9},
  year      = {2021},
  publisher = {The Science and Information Organization},
  author    = {Nishika and Kamna Solanki and Sandeep Dalal},
  doi       = {10.14569/IJACSA.2021.0120952},
  url       = {https://doi.org/10.14569/IJACSA.2021.0120952}
}