Development of an Optimized AES-ECIES Hybrid Encryption Algorithm for Secure and Energy-Efficient Data Transmission In E-Learning Platforms

The rise in wireless and cloud-based e-learning has created new cybersecurity issues surrounding secure data transfer, user privacy, and efficient power usage. Many traditional encryption techniques are not appropriate for mobile learning environments due to compromises in security, computational complexity, and energy consumption. To solve these problems, an optimized hybrid encryption scheme (AES-ECIES) to secure and energy-efficient data transmission in an e-learning platform is proposed in this paper. The proposed encryption model combines the Advanced Encryption Standard (AES) with the Elliptic Curve Integrated Encryption Scheme (ECIES), which are used for high-speed symmetric data encryption and lightweight and secure public-key-based session key exchange. The proposed framework works on secure authentication, dynamic session key creation, ECIES-based key protection, AES-based educational data encryption, and secure wireless communication. The hybrid integration improves confidentiality, authentication, and integrity verification and resistance to attacks, but also reduces the process delay and transmission overhead.Experimental results show that the proposed model provides a significant improvement in the performance of communication over other conventional encryption models, such as AES, RSA-AES, ECC-based encryption, and the Blowfish model. The proposed AES-ECIES framework proved to be more efficient in terms of computational time, with encryption time of 34.2 ms and decryption time of 32.6 ms. The framework also cut down the energy consumption to 1.74 joules with a high throughput of 86.9 Mbps. In addition, the proposed model also showed a high level of security accuracy (98.7%), demonstrating that it is highly resistant to data tampering, eavesdropping, unauthorized access, and replay attacks in wireless education. The ablation analysis results proved that the overall performance and security robustness of the hybrid integration of AES and ECIES are significantly enhanced. The results show that the proposed framework is a scalable, reliable, and energy-efficient cybersecurity solution for smart e-learning platforms of the next generation.

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