ArticleScientific reports2025
An integrated CSPPC and BiLSTM framework for malicious URL detection.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- A scalable hybrid computational intelligence framework with bio inspired optimization for high dimensional malicious URL inference.Scientific reports · 2026Article
- Metadata driven malicious URL detection using RoBERTa large and multi source network threat intelligence.Scientific reports · 2026Article
- Lightweight malicious URL detection using deep learning and large language models.Scientific reports · 2025Article
- GCSA-ResNet: a deep neural network architecture for Malware detection.Scientific reports · 2025Article
- Explainable artificial intelligence driven insights into smoking prediction using machine learning and clinical parameters.Scientific reports · 2025Article
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8 authors.
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Abstract
With the rapid development of the internet, phishing attacks have become more diverse, making phishing website detection a key focus in cybersecurity. While machine learning and deep learning have led to various phishing URL detection methods, many remain incomplete, limiting accuracy. This paper proposes CSPPC-BiLSTM, a malicious URL detection model based on BiLSTM (Bidirectional Long Short-Term Memory, BiLSTM). The model processes URL character sequences through an embedding layer and captures contextual information via BiLSTM. By integrating CBAM (Convolutional Block Attention Module, CBAM), it applies channel and spatial attention to highlight key features and transforms URL sequence features into a spatial matrix. The SPP (Spatial Pyramid Pooling, SPP) module enables multi-scale pooling. Finally, a fully connected layer fuses features, and dropout regularization enhances robustness. Compared to CharBiLSTM, CSPPC-BiLSTM significantly improves detection accuracy. Evaluated on two datasets, Grambedding (balanced) and Mendeley AK Singh 2020 phish (imbalanced)-and compared with six baselines, it demonstrates strong generalization and accuracy. Ablation experiments confirm the critical role of CBAM and SPP in boosting performance.
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