English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The widespread use of the Internet has revolutionized information retrievalmethods. However, this transformation has also given rise to a significantcybersecurity challenge: the rapid proliferation of malicious URLs, which serveas entry points for a wide range of cyber threats. In this study, we present anefficient pre-training model-based framework for malicious URL detection.Leveraging the subword and character-aware pre-trained model, CharBERT, as ourfoundation, we further develop three key modules: hierarchical featureextraction, layer-aware attention, and spatial pyramid pooling. Thehierarchical feature extraction module follows the pyramid feature learningprinciple, extracting multi-level URL embeddings from the different Transformerlayers of CharBERT. Subsequently, the layer-aware attention module autonomouslylearns connections among features at various hierarchical levels and allocatesvarying weight coefficients to each level of features. Finally, the spatialpyramid pooling module performs multiscale downsampling on the weightedmulti-level feature pyramid, achieving the capture of local features as well asthe aggregation of global features. The proposed method has been extensivelyvalidated on multiple public datasets, demonstrating a significant improvementover prior works, with the maximum accuracy gap reaching 8.43% compared to theprevious state-of-the-art method. Additionally, we have assessed the model'sgeneralization and robustness in scenarios such as cross-dataset evaluation andadversarial attacks. Finally, we conducted real-world case studies on theactive phishing URLs.

Malicious URL Detection via Pretrained Language Model Guided Multi-Level Feature Attention Network