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Jinsha Lawrence

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Open access 2026

STEGOCRYPTNET: A DEEP LEARNING AND NTRU-BASED CRYPTOGRAPHIC IMAGE STEGANOGRAPHY FRAMEWORK

In present digital age, protecting and safeguarding essential information against unauthorized access is considered crucial. Thus, image steganography is regarded as they offer strong approach fo concealing the secret data within images, allowing secure communication. Henceforth, this paper focuses on developing a Secu...

Jinsha Lawrence, Dinesh Kumar Budagam, Kanmani P et al. · 0 citations
Conference Aug 2026

Adaptive Feature Optimization for Machine Learning-Based Malware Family Classification

Malware family classification is essential for understanding malicious software behaviour and supporting cybersecurity analysis. Existing machine learning approaches have demonstrated promising classification performance; however, many rely on high-dimensional feature sets that increase computational complexity and inc...

V. R, Vishwa M, T. M et al. · 0 citations
Conference Aug 2026

A Trust-Aware ICN–Block Chain Framework for Secure VANETS Enabled by PBFT

The vehicular ad-hoc network (VANET) provides a way for drivers or vehicles to communicate with each other in a more effective manner, however, there are still many issues with respect to different aspects of security, trust management, and delivering information reliably between two nodes. This study presents a way to...

Jinsha Lawrence, S. Pradeepa, R. T. Kumar et al. · 0 citations

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