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A novel optimized neural network model for cyber attack detection using enhanced whale optimization algorithm.

Koganti Krishna Jyothi1, Subba Reddy Borra2, Koganti Srilakshmi3

  • 1Department of Computer Science and Engineering, Geethanjali College of Engineering and Technology, Hyderabad, TS, 501301, India.

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Credential stuffing attacks threaten online security. A new Enhanced Whale Optimization Algorithm-Artificial Neural Network (EWOA-ANN) model effectively detects and predicts these cyber threats, enhancing account protection.

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Area of Science:

  • Cybersecurity and Network Security
  • Artificial Intelligence in Cybersecurity
  • Computational Intelligence

Background:

  • Cybersecurity is paramount in our interconnected digital world.
  • Credential stuffing attacks, leveraging stolen credentials, pose a significant threat to online account security.
  • Widespread reuse of passwords across multiple platforms exacerbates this vulnerability.

Purpose of the Study:

  • To address the challenges of detecting and predicting credential stuffing attacks.
  • To introduce a novel model for enhanced cybersecurity defense.
  • To improve the security of online accounts against unauthorized access.

Main Methods:

  • Development of a novel Enhanced Whale Optimization Algorithm (EWOA) for training.
  • Integration of EWOA with an Artificial Neural Network (ANN) to create the EWOA-ANN model.
  • Utilizing the EWOA-ANN model for credential stuffing attack detection and prediction.

Main Results:

  • The proposed EWOA-ANN model demonstrates effectiveness in identifying credential stuffing attacks.
  • The model shows promise in predicting the occurrence and nature of these cyber threats.
  • Empirical comparisons are planned to validate the model's performance against existing security analyses.

Conclusions:

  • The EWOA-ANN model offers a robust solution for combating credential stuffing cyberattacks.
  • This approach enhances the detection and prediction capabilities crucial for online security.
  • The study contributes a novel optimization technique for improving neural network performance in cybersecurity applications.