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@article{200899,
        author = {Ms P Subasri and Hariharan K and Hariprasath M and Thiruvarasan G},
        title = {A Hybrid Ai Pipeline for Detecting Adversarial Access and Generating Deceptive Document Ecosystems},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {no},
        pages = {1-9},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=200899},
        abstract = {Intellectual property (IP) constitutes the lifeblood of modern organizations, encompassing creative works, inventions, proprietary research, and confidential business knowledge that sustain innovation and competitive advantage. With the rise of large-scale automation and intelligent data-mining tools, cyber adversaries now target IP repositories using machine-driven classification, clustering, and topic-extraction pipelines capable of rapidly identifying high-value information.
To counter these emerging threats, this project introduces DARD (Decoy Approaches for Robust Protection against IP Theft), a deception-oriented IP protection framework that employs a Variational Autoencoder (VAE) for anomaly detection and NLP-driven document manipulation techniques including TF-IDF feature extraction, K-Means clustering, and LDA topic modeling. The system misleads automated adversarial tools by generating deceptive document ecosystems featuring keyword permutation, selective removal, and topic substitution, thereby preserving the confidentiality of sensitive IP while maintaining seamless access for legitimate users.},
        keywords = {Spam Detection, Logistic Regression, Machine Learning, TF-IDF, Text Preprocessing, Email Filtering, Bag of Words, Natural Language Processing, Binary Classification, Cybersecurity.},
        month = {May},
        }