Deft-IP: Decoy Enhanced Framework for Threat Protection in Intellectual Property
DOI:
https://doi.org/10.15662/IJEETR.2026.0802378Keywords:
Intellectual Property (IP), Cyber security, Decoy-Based Defense, Variational Auto encoder (VAE), Natural Language Processing (NLP), TF-IDF, K-Means Clustering, Latent Dirichlet Allocation (LDA), Anomaly Detection, Data ProtectionAbstract
Intellectual Property (IP) represents a critical asset for modern organizations, encompassing sensitive information such as proprietary designs, research data, and strategic knowledge. With the rapid advancement of cyber technologies, IP repositories have become prime targets for sophisticated attacks, particularly automated data extraction methods that utilize machine learning techniques for document classification and topic modeling. Traditional security mechanisms, while effective at restricting access, often fail to prevent intelligent adversaries from analyzing and inferring valuable information once access is obtained.
To address this challenge, this paper proposes DEFT-IP (Decoy Enhanced Framework for Threat Protection in Intellectual Property), an intelligent and proactive defense system designed to safeguard sensitive documents. The framework employs a Variational Autoencoder (VAE) to monitor user behavior and detect anomalies indicative of potential threats. Upon identifying suspicious activity, the system dynamically generates decoy documents by manipulating content through keyword shuffling, topic alteration, and controlled data modification.
The proposed system integrates advanced Natural Language Processing (NLP) techniques, including TF-IDF, K-Means clustering, and Latent Dirichlet Allocation (LDA), to create semantically plausible yet misleading data. This approach disrupts automated analysis tools used by attackers while preserving seamless access for legitimate users. Experimental outcomes demonstrate that DEFT-IP significantly enhances the confidentiality, robustness, and resilience of intellectual property systems against emerging cyber threats, offering a novel direction for intelligent data protection
References
1. The future scope of the project is expansive, with potential for continual improvement and adaptation to evolving technological landscapes.
2. Integration with Blockchain Technology:Use blockchain to create a tamper-proof log of all access and changes to IP documents, ensuring better transparency and traceability.
3. Multi-Factor Authentication (MFA):Introduce multi-factor authentication to provide an additional layer of security for accessing the IP repository, reducing the risk of unauthorized access.
4. Cross-Platform Integration: Enable integration with various platforms (e.g., mobile devices) to allow secure access and management of documents across different devices and environments.
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