Deep Learning-based Intrusion Detection: A Novel Approach for Identifying Brute-Force Attacks on FTP and SSH Protocol

As networks continue to expand rapidly, the number and diversity of cyberattacks are also increasing, posing a significant challenge for organizations worldwide. Consequently, brute-force attacks targeting FTP and SSH protocols have become more prevalent. IDSes offer an essential tool to detect these attacks, providing traffic analysis and system monitoring. Traditional IDSes employ signatures and anomalies to monitor information flow for malicious activity and policy violations; however, they often struggle to effectively identify unknown or novel patterns. In response, we propose a novel intelligent approach based on deep learning to detect brute-force attacks on FTP and SSH protocols. We conducted an extensive literature review and developed a metric to compare our work with existing literature. Our findings indicate that our proposed approach achieves an accuracy of 99.9%, outperforming other comparable solutions in detecting brute-force attacks.

@article{Alotibi2023,
  title     = {Deep Learning-based Intrusion Detection: A Novel Approach for Identifying Brute-Force Attacks on FTP and SSH Protocol},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {6},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Noura Alotibi and Majid Alshammari},
  doi       = {10.14569/IJACSA.2023.0140612},
  url       = {https://doi.org/10.14569/IJACSA.2023.0140612}
}