A Novel Cyber-attack Leads Prediction System using Cascaded R2CNN Model

Novel prediction systems are required in almost all internet-connected platforms to safeguard the user information to get hacked by intermediate peoples. Finding the real impacted factors associated with the Cyber-attack probes are being considered for research. The proposed methodology is derived from various literature studies that motivated to find the unique prediction model that shows improved accuracy and performance. The proposed model is represented as R2CNN that acts as the cascaded combination of Gradient boosted regression detector with recurrent convolution neural network for pattern prediction. The given input data is the collection of various applications engaged with the wireless sensor nodes in a smart city. Each user connected with a certain number of applications that access the authorization of the device owner. The dataset comprises device information, the number of connectivity, device type, simulation time, connectivity duration, etc. The proposed R2CNN extracts the features of the dataset and forms a feature mapping that related to the parameter being focused on. The features are tested for correlation with the trained dataset and evaluate the early prediction of Cyber-attacks in the massive connected IoT devices.

@article{Prabha2022,
  title     = {A Novel Cyber-attack Leads Prediction System using Cascaded R2CNN Model},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {2},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {P. Shanmuga Prabha and S. Magesh Kumar},
  doi       = {10.14569/IJACSA.2022.0130260},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130260}
}