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Research Paper | Computer Science | Volume 15 Issue 2, February 2026 | Pages: 1500 - 1509 | India
Advanced Encryption Strategies for Modern Network Security and Threat Mitigation
Abstract: Recent digital age has led to pressure on the strength of interconnected systems and necessity to build efficient security systems, which are dynamic. The traditional encryption techniques though good lack the scalability, intelligence and real-time flexibility that is required to invoke the present day cyber threats. The present paper introduces a Hybrid Encryption Framework (DL-HEF) that is an Advanced Deep Learning-based framework that is a mix of deep neural intelligence and multi-layer encryption plans to enhance the degree of data confidentiality, data integrity, and threat-resilience. The given model is dynamic and optimizes encryption and decryption activities, at the same time, identifying anomalies with the help of an intelligent attack prediction tool. When compared to the traditional algorithms like AES, RSA, and the hybrid deep learning algorithms, it can be seen that the DL-HEF is 30 times faster in encryption speed, 25 times faster in latency, and 98 percent more effective at detecting threats. The findings of the experiment supplemented by several tables and graphical assessments prove that the framework can be used to attain high performance in cloud, IoT, and large-scale network setups. On the whole, this paper confirms that DL-HEF is an inclusive solution to attaining secure, scalable, and intelligent network protection against changing cybersecurity threat.
Keywords: Network Security, Advanced Encryption, Deep Learning, Hybrid Cryptography, Threat Detection, Blockchain Integration, Cloud Security, IoT Security, Cyberattack Mitigation, AI-based Encryption
How to Cite?: Soniya, Dr. Tilak Raj Rohilla, "Advanced Encryption Strategies for Modern Network Security and Threat Mitigation", Volume 15 Issue 2, February 2026, International Journal of Science and Research (IJSR), Pages: 1500-1509, https://www.ijsr.net/getabstract.php?paperid=SR26222165829, DOI: https://dx.dx.doi.org/10.21275/SR26222165829