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@article{207292,
        author = {Richa Gupta and Saubhagya Gupta and Krishna Gupta and Ms Kajal},
        title = {Enhancing Predictive Threat Hunting Using Block Chain and Machine Learning},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {226-231},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=207292},
        abstract = {This paper presents a predictive cybersecurity framework that integrates Hyperledger Fabric with machine learning to enable tamper-resistant threat hunting. Security logs and network events are immutably stored on the blockchain to ensure data integrity and provenance, forming a reliable dataset for model training. After preprocessing and class-imbalance handling, models including XG Boost, Random Forest, Logistic Regression, and Support Vector Machine are trained to detect ransomware, phishing, lateral movement, and unauthorized access. Results show improved detection accuracy and fewer false positives, with XG Boost and Random Forest performing best. The study demonstrates that blockchain-verified data combined with machine learning enables proactive and trustworthy cybersecurity analytics in distributed environments.},
        keywords = {Blockchain, Hyperledger Fabric, Cybersecurity Analytics, Machine Learning, Threat Hunting, Tamper-Resistant Logs, XG Boost, Distributed Environments.},
        month = {July},
        }