GHIRIJHA V P , LAKSHMI PRIYA S J, NIGIL CHRYSO PRAISES N C
Abstract
The rapid growth of web applications has led to an exponential increase in the volume and diversity of data handled online, including text, images, audio, video, and structured logs— collectively referred to as multi-modal data. Traditional security mechanisms often apply uniform encryption strategies that fail to address the distinct characteristics and threat profiles of different data modalities. This paper proposes an intelligent cybersecurity framework that integrates adaptive encryption, machine learning–based threat assessment, and secure key management to provide robust, end-to-end protection for multi-modal data in web applications. The framework dynamically selects encryption algorithms and key lengths based on data type, sensitivity, and contextual risk, thereby optimizing both security strength and system performance. Experimental evaluation demonstrates improved resistance to common web-based attacks while maintaining acceptable latency and scalability.
This paper presents an intelligent cybersecurity framework for multi-modal data encryption and decryption in web applications. By combining adaptive cryptographic techniques with machine learning–based risk assessment, the framework addresses the limitations of traditional static security approaches. Future work will focus on incorporating advanced deep learning models, supporting edge and cloud-native deployments, and conducting large-scale real-world evaluations.
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How to Cite This Paper
GHIRIJHA V P , LAKSHMI PRIYA S J, NIGIL CHRYSO PRAISES N C (2026). AN INTELLIGENT CYBERSECURITY FRAMEWORK FOR MULTI-MODAL DATA ENCRYPTION AND DECRYPTION IN WEB APPLICATIONS. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
