Integrating Digital Forensics with Intelligent Web Design: Deep Learning for Event Query Classification and Framework Evaluation
DOI:
https://doi.org/10.15662/IJRAI.2022.0505003Keywords:
Digital Forensics, Intelligent Web Design, Deep Learning, Event Query Classification, Framework Evaluation, Transformer Networks, BiLSTM, Cybersecurity Analytics, Fuzzy Logic, Human–Computer InteractionAbstract
The convergence of digital forensics and intelligent web design has created new opportunities for adaptive, secure, and user-centric cyber investigation environments. This paper proposes a Deep Learning-based Event Query Classification and Framework Evaluation Model that integrates forensic intelligence directly into web-based investigative platforms. The model employs Bidirectional LSTM (BiLSTM) and Transformer-based neural architectures to analyze, categorize, and prioritize forensic event queries in real time, enhancing both the accuracy and responsiveness of digital investigations. By embedding these capabilities within an intelligent web design framework, the system ensures seamless interaction between data visualization, forensic evidence retrieval, and AI-driven analysis. The framework evaluation module leverages multi-criteria decision analysis (MCDA) and fuzzy logic inference to assess the performance, usability, and reliability of the forensic web interface across dynamic scenarios. Experimental validation demonstrates that the proposed model achieves superior event classification accuracy, reduced query latency, and enhanced decision transparency compared to traditional forensic platforms. This integration bridges the gap between advanced deep learning analytics and interactive web-based forensics, paving the way for intelligent, adaptive, and secure forensic web ecosystems.
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