Enhancing Cloud Security: An Optimization-based Deep Learning Model for Detecting Denial-of-Service Attacks

DoS (Denial-of-Service) attacks pose an imminent threat to cloud services and could cause significant financial and intellectual damage to cloud service providers and their customers. DoS attacks can also result in revenue loss and security vulnerabilities due to system disruptions, interrupted services, and data breaches. However, despite machine learning methods being the research subject for detecting DoS attacks, there has not been much advancement in this area. As a consequence of this, there is a requirement for additional research in this field to create the most effective models for the detection of DoS attacks in cloud-based environments. This research paper suggests a deep convolutional generative adversarial network as an optimization-based deep learning model for identifying DoS bouts in the cloud. The proposed model employs Deep Convolutional Generative Adversarial Networks (DCGAN) to comprehend the spatial and temporal features of network traffic data, thereby enabling the attack detection of patterns indicative of DoS assaults. Furthermore, to make the DCGAN more accurate and resistant to attacks, it is trained on a massive collection of network traffic data. Moreover, the model is optimized via backpropagation and stochastic gradient descent to lessen the loss function, quantifying the gap between the simulated and observed traffic volumes. The testing findings prove that the suggested model is superior to the most recent technology methods for identifying cloud-based DoS assaults in Precision and the rate of false positives.

@article{Alhazmi2023,
  title     = {Enhancing Cloud Security: An Optimization-based Deep Learning Model for Detecting Denial-of-Service Attacks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {7},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Lamia Alhazmi},
  doi       = {10.14569/IJACSA.2023.0140737},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140737}
}