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Artificial intelligence-driven cybersecurity: enhancing malicious domain detection using attention-based deep

Fatimah Alhayan¹, Asma Alshuhail², Ahmed Omer Ahmed Ismail³

¹Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.

Scientific Reports

|July 3, 2025

Summary

This summary is machine-generated.

This study introduces an AI-driven method, EMDD-ADLMOA, to detect malicious domains by analyzing their behavior. The model achieved 98.52% accuracy, significantly improving cybersecurity defenses against online threats.

Keywords:

Artificial intelligence Cybersecurity Deep learning Feature selection Malicious domain detection

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Area of Science:

Cybersecurity
Artificial Intelligence
Network Security

Background:

Malicious domains are critical for cyberattacks.
The Domain Name System (DNS) is vital for internet functionality.
Detecting malicious domains is crucial for network security.

Purpose of the Study:

To propose an enhanced technique for detecting malicious domains.
To leverage artificial intelligence for improved cybersecurity.
To develop a robust and scalable malware detection system.

Main Methods:

Pre-processing data using min-max scaling.
Feature selection using the quantum-inspired firefly algorithm (QIFA).
Classification using a hybrid Temporal Convolutional Network (TCN) and Bi-directional Long Short-Term Memory (BiLSTM) with Squeeze-and-Excitation Attention (SEA).
Hyperparameter tuning with the Parrot Optimization (PO) algorithm.

Main Results:

The proposed EMDD-ADLMOA technique demonstrated superior performance.
Achieved an accuracy of 98.52% in detecting malicious domains.
Outperformed existing methods in experimental validation.

Conclusions:

The EMDD-ADLMOA technique effectively enhances malicious domain detection.
AI-based models offer a promising approach for robust cybersecurity.
The study contributes a scalable and efficient solution for identifying and mitigating online threats.