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@article{192354,
        author = {Praveen G and Sanjay S and Lakshmikanth M P and Mohith Kumar R and Mohanapriya M},
        title = {Automated Hash Algorithm Detection Using Random Forest Classifier},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {9},
        pages = {1259-1263},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=192354},
        abstract = {It is an automated system for identifying the cryptographic hashing algorithm used to generate a given hash value by leveraging machine learning techniques. Hash functions are fundamental to cybersecurity applications such as password storage, digital signatures, and data integrity verification. Manual identification of hash algorithms is time-consuming and error-prone, especially with the growing number of algorithms. The proposed approach employs a Random Forest classifier trained on features extracted from hash strings, including length, entropy, and character distribution. Experimental results demonstrate high classification accuracy, making the system suitable for digital forensics and security analysis.},
        keywords = {Cryptographic Hash Functions, Random Forest, Machine Learning, Cybersecurity, Digital Forensics},
        month = {February},
        }