CESSO-HCRNN: A Hybrid CRNN With Chaotic Enriched SSO-based Improved Information Gain to Detect Zero-Day Attacks

Hackers use the vulnerability before programmers have a chance to fix it, which is known as a zero-day attack. Zero-day attackers have a variety of abilities, including the ability to alter files, control machines, steal data, and install malware or adware. When a series of complex assaults uses one or more zero-day exploits, the result is a zero-day attack path. Timely assessment of zero-day threats might be enabled by early detection of zero-day attack pathways. To detect this zero-day attack, this paper introduced a Chaotic Enriched Salp Swarm Optimization (CESSO) with the help of a hybrid Convolutional Recursive Neural Network (HCRNN) is implemented. The input data is retrieved from two datasets called IDS 2018 Intrusion CSVs (CSE-CIC-IDS2018) and NSL-KDD. The data is pre-processed with the help of data cleaning and normalization. A unique hybrid feature selection method that is based on the CESSO and Information Gain(IG) is introduced. The CESSO is also used to improve the Recursive Neural Network (RNN) performance to produce an optimized RNN. The selected features are classified, and prediction is performed using the hybrid Convolutional Neural Network (CNN) with RNN called HCRNN. The implementation of the zero-day attack is performed using MATLAB software. The accuracy achieved for dataset 1 is 98.36%, and for dataset 2 is 97.14%.

@article{Roy2023,
  title     = {CESSO-HCRNN: A Hybrid CRNN With Chaotic Enriched SSO-based Improved Information Gain to Detect Zero-Day Attacks},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {11},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Dharani Kanta Roy and Ripon Patgiri},
  doi       = {10.14569/IJACSA.2023.01411129},
  url       = {https://doi.org/10.14569/IJACSA.2023.01411129}
}