A BERT-Enhanced Exploration of Web and Mobile Request Safety Through Advanced NLP Models and Hybrid Architectures

Abstract

In the rapidly evolving landscape of digital technology, the security of web and mobile applications stands paramount. As these platforms become increasingly integrated into our daily lives, the need for robust safety measures becomes imperative. This research paper delves into the intricate realm of web and mobile request safety, unraveling a multi-faceted exploration that combines traditional feature engineering with state-of-the-art machine learning models. Beginning with foundational models like TextCNN and TextRNN, we scrutinize their effectiveness in discerning the safety of requests. Advancing our investigation, we delve into the capabilities of sophisticated architectures, including Bidirectional LSTMs, DistilBERT, and RoBERTa. Beyond individual assessments, we introduce hybrid models that synergize the strengths of various approaches, establishing a comprehensive defense against emerging security threats. Throughout this research, we navigate the intricacies of model training, evaluation, and performance metrics. From accuracy and precision to recall and confusion matrices, each metric paints a nuanced picture of the efficacy of these models in ensuring the safety of web and mobile interactions. In a world where cyber threats loom large, the significance of this research lies not only in its technical contributions but also in its practical implications. By providing insights into innovative strategies for enhancing the security and resilience of digital applications, this paper contributes to the ongoing discourse on fortifying the digital infrastructure.

Keywords

• Web and mobile security
• request safety
• machine learning
• natural language processing
• cybersecurity