Proactive Detection of Phishing and Malicious Link

Abstract

In Deep Learning is a branch of Artificial Intelligence (AI) that has proven effective in various predictive tasks, including identifying malicious URLs and phishing links. It recognize Complex patterns in text, picture, sound and other data to produce accurate insights and predictions. Existing systems often rely on unsupervised algorithms to detect phishing links; These methods typically suffer from lower prediction accuracy and are limited in scope, focusing only on phishing detection. To address these limitations, We Prove a hybrid model combining Deep Learning and Transfer Learning algorithms has been developed. This approach enhances the accuracy of predictions. Then, the hybrid model extends the detection capabilities to both malicious and phishing links, ensuring comprehensive protection .

Introduction

1. Threat of Malicious URLs and Phishing:
Malicious URLs pose significant cybersecurity threats by facilitating malware distribution, phishing, and fraud. These deceptive links often appear legitimate, making them hard to detect. Phishing links, embedded in emails or websites, aim to steal sensitive data using social engineering tactics. Due to the growing complexity of these threats, user education and awareness are critical, alongside tools like email filters, anti-phishing software, and browser protection mechanisms.

2. Deep Learning Models for Detection:
To counter these threats, a hybrid deep learning model combining Bidirectional Long Short-Term Memory (BiLSTM) and Bidirectional Gated Recurrent Unit (BiGRU) is proposed. These models process URL sequences in both directions, allowing them to capture long-range dependencies and contextual patterns. BiLSTM retains complex temporal information, while BiGRU enhances efficiency by reducing computational load.

3. Architecture and Features:
The model architecture includes:

• Preprocessing (cleaning, tokenization, normalization)

• Word Embedding (e.g., Word2Vec, FastText)

• Feature Extraction from lexical, host-based, and content-based indicators

• BiLSTM-BiGRU Layers for pattern learning

• Dropout and Fully Connected Layers to prevent overfitting and aid classification

• Softmax Output Layer for final predictions

4. Ensemble Voting Mechanisms:
The system uses soft and hard voting to enhance prediction reliability:

• Soft Voting averages probability outputs.

• Hard Voting chooses the majority class prediction.
  This ensemble technique boosts the system's robustness and accuracy.

5. Dataset and Training:
Training uses publicly available datasets (e.g., PhishTank, OpenPhish) with diverse phishing and legitimate URLs. Data balancing (e.g., SMOTE) and augmentation improve generalization. Preprocessing ensures clean and uniform input for model training.

6. Performance Metrics and Evaluation:
The model's effectiveness is measured using:

• Accuracy (overall correctness)

• Precision (true positive rate for phishing)

• Recall and F1-Score (balance between precision and detection capability)

• Loss Function (Cross-Entropy) to guide optimization

Comparisons with traditional classifiers (e.g., SVM, Random Forest) demonstrate the hybrid model’s superior performance in detecting zero-day phishing attacks.

7. Real-Time Prediction:
Once trained, the model can predict and flag malicious URLs in real-time. URLs surpassing a threat probability threshold are marked as phishing. Detected links can be automatically blacklisted to prevent user harm.

8. Key Benefits and Challenges:
The model provides a scalable, accurate, and adaptive cybersecurity solution. However, like other deep learning models, it must address overfitting risks, especially in complex architectures. Regularization and proper data handling are essential for maintaining generalization across unseen data.

Conclusion

In conclusion, an era of escalating cyber threats, this research introduces a robust phishing and malicious URL detection model leveraging transfer learning with BiLSTM and BiGRU networks. By capturing sequential dependencies and temporal patterns, the model enhances detection accuracy beyond traditional approaches. The integration of advanced feature selection and hyperparameter optimization ensures optimal performance, making the system adaptable to evolving threats. This research contributes to the field by presenting a scalable, intelligent, and adaptive solution, paving the way for enhanced digital security against phishing attacks and malicious links. Future work can focus on integrating real-time threat intelligence to detect emerging phishing URLs dynamically. Enhancing the model with ensemble learning using CNNs, Transformers, or Attention Mechanisms can improve accuracy.

References

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Copyright

Copyright © 2025 Dr. Kavitha V, Dr. R. G. Suresh Kumar, Mr. Ragu K, 4 , Mr. Thamizhselvan K, Mr. Sathiyan D. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.