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<p>Cyber-attacks pose a significant risk to digital infrastructure, resulting in losses at both individual and organizational levels, underscoring the need for proactive and intelligent defense mechanisms. This study proposes a hybrid Cyber Threat Intelligence (CTI) system integrating an immutable blockchain ledger, adaptive machine-learning models, and natural-language processing algorithms for timely detection, classification, and secure sharing of threat data. The system forecasts future attacks by analyzing aggregated data and recommending mitigation strategies. A BERT-based model, combined with spaCy and regular expressions for extracting Indicators of Compromise (IOCs) from unstructured data, achieved 95% accuracy and a 95.7% F1-score, with a 55% latency reduction (from 120ms to 54ms for 200 reports). Validation used 10-fold cross-validation with paired t-tests across 10,000 Monte Carlo simulations (t = 3.45, <i>p</i> &lt; 0.001, Cohen’s d ranging 0.76–1.12 from heatmaps) on CIC-IDS2017 and UNSW-NB15 datasets. The Cross-Dataset Robustness Index (CRI) confirmed strong generalization, with BERT at 0.999, slightly outperforming LSTM (0.998), SVM (0.95), and Naïve Bayes (0.92). The system excels in high-volume data processing, event correlation, and threat detection/response rates. This scalable solution suits Security Operations Centers (SOCs), IoT environments, and financial cybersecurity, providing robust unstructured data handling and adaptability to evolving threats.</p>

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BERT-spaCy hybrid NLP and blockchain-enhanced adaptive CTI for IOC extraction and threat prediction

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月2日

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  • Shailendra Mishra,
  • Ruba Ahmed Alfahidah,
  • Fayez Alharbi

摘要

Cyber-attacks pose a significant risk to digital infrastructure, resulting in losses at both individual and organizational levels, underscoring the need for proactive and intelligent defense mechanisms. This study proposes a hybrid Cyber Threat Intelligence (CTI) system integrating an immutable blockchain ledger, adaptive machine-learning models, and natural-language processing algorithms for timely detection, classification, and secure sharing of threat data. The system forecasts future attacks by analyzing aggregated data and recommending mitigation strategies. A BERT-based model, combined with spaCy and regular expressions for extracting Indicators of Compromise (IOCs) from unstructured data, achieved 95% accuracy and a 95.7% F1-score, with a 55% latency reduction (from 120ms to 54ms for 200 reports). Validation used 10-fold cross-validation with paired t-tests across 10,000 Monte Carlo simulations (t = 3.45, p < 0.001, Cohen’s d ranging 0.76–1.12 from heatmaps) on CIC-IDS2017 and UNSW-NB15 datasets. The Cross-Dataset Robustness Index (CRI) confirmed strong generalization, with BERT at 0.999, slightly outperforming LSTM (0.998), SVM (0.95), and Naïve Bayes (0.92). The system excels in high-volume data processing, event correlation, and threat detection/response rates. This scalable solution suits Security Operations Centers (SOCs), IoT environments, and financial cybersecurity, providing robust unstructured data handling and adaptability to evolving threats.