Neural Network Model Using An Enhanced Whale Optimization Method For Cyber Threat Detection

Authors

  • Asra Sarwath Assistant Professor, Dept. of CS&E, Khaja Bandanawaz University, Kalaburagi, Karnataka, India Author
  • Dr. Raafiya Gulmeher Assistant Professor, Dept. of CS&E, Khaja Bandanawaz University, Kalaburagi, Karnataka, India Author
  • Zeenath Sultana Assistant Professor, Dept. of CS&E, Khaja Bandanawaz University, Kalaburagi, Karnataka, India Author

DOI:

https://doi.org/10.47392/IRJAEH.2025.0022

Keywords:

Credential stuffing, Neural Network (NN), Cyber-attack

Abstract

In our modern, highly-connected society, cybersecurity is of the utmost importance. With the rapid advancement and increasing integration of technology into our daily lives, the need of cyber security cannot be emphasized enough. Cybersecurity is vital for individual’s protection. Credential stuffing is a sort of cyber assault whereby attackers use previously obtained usernames, keywords and passwords to unlawfully invoke user accounts across many websites. This is plausible as many individuals utilize identical passwords and usernames across several websites. The proposed Enhanced Whale Optimization Algorithm Neural Network (EWOA-NN) model may address the issues of failure detection, prediction, and credential stuffing attacks. A unique optimization method called the neural network is trained using EWOA. After confirming the efficacy of the proposed attack identification model, we will conduct an empirical comparison with respect to certain security research.

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Published

2025-02-14

How to Cite

Neural Network Model Using An Enhanced Whale Optimization Method For Cyber Threat Detection. (2025). International Research Journal on Advanced Engineering Hub (IRJAEH), 3(02), 164-171. https://doi.org/10.47392/IRJAEH.2025.0022

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