Temporal Intelligence in AI-Enhanced Cyber Forensics using Time-Based Analysis for Proactive Threat Detection
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Abstract
To detect and address threats proactively, this study investigates the incorporation of temporal intelligence into AI-enhanced cyber forensics. Temporal intelligence makes timelines, recognizes patterns, and projects future risks by utilizing historical data. The method provides adaptive algorithms for ongoing monitoring, optimizes incident response, and preserves forensic evidence with precise timestamps. Temporal analysis, anomaly identification, incident response optimization, continuous monitoring, and behavioral analysis are highlighted in-depth throughout the flowchart phases. using the methodology's integration of machine learning and temporal intelligence, developing cyber risks can be proactively identified and mitigated using a strong cyber forensics framework. Machine learning, natural language processing, deep learning, and other AI-enhanced cyber forensics tools show varied applications and capacities across critical parameters. Time-Based Analysis shows to be quite successful, especially when it comes to temporal data processing and dynamic threat detection. The study's conclusion emphasizes the flexibility of Time-Based Analysis and Machine Learning, underscoring the continuous need for research and development to improve these methods and handle new cyberthreats in the dynamic field of cybersecurity.
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