Design of Mutual Authentication Method for Deep Learning Based Hybrid Cryptography to Secure Data in Cloud Computin
Bibliographic record
Abstract
In today's highly competitive environment, it might be difficult to keep sensitive data or information safe. Sharing sensitive information between parties in a cloud environment requires a high level of trust between them. There are numerous methods for achieving data and information security, including cryptography, steganography, etc. This research introduces a new approach to mutual authentication by using a pre-trained model of a convolutional neural network (CNN) to identify malicious activity on the internet. In this research paper, we present a novel approach for enhancing the security of data in cloud computing environments through the design of a mutual authentication method for deep learning-based hybrid cryptography. Our approach combines the strengths of hybrid cryptography and the power of deep learning to provide a robust and adaptable solution for securing data in the cloud. One of the key innovations of our approach is the integration of a pre-trained convolutional neural network (CNN) model. This CNN plays a pivotal role in identifying and mitigating malicious activities on the internet that could pose a threat to cloud-based data. By continuously monitoring network traffic and data patterns, the CNN contributes to the proactive defense mechanism of our system. Secure communication between the involved parties is ensured by combining cryptography with authentication for key agreements. However, no known security method has simultaneously provided a high level of security and a fast execution time. When compared to older encryption systems, hybrid encryption techniques are far superior in terms of providing peace of mind for users. In order to provide robust security, this paper presents hybrid encryption procedures (HEA) by combination of symmetric key (Message Authentication Code [MAC]) and asymmetric key cryptographic procedures (Modified and Enhanced Lattice-Based Cryptography [MELBC]). Results from experiments show that the suggested HEA algorithm offers more security than competing security algorithms.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".