Internet security and data protection should be guaranteed for all users. Therefore, security is a major concern when exposing information to networks. In todays world, the information is never enough protected the way it should be. Cryptography is one of the most effective and efficient components of network security. Cryptography is a technique to apply ensure the secure and reliable transaction between the sender and the receiver when transferring unintelligible information. Moreover, only the authorized receiver can have the right to decrypt the information that was sent and encrypted by the sender. The cryptography plays an essential role in order to provide security to these networks. In this paper, we observe encryption as well as decryption time of different algorithms with the random size of data packets. Firstly, this work introduces the fundamental concepts of Cryptography: encryption and decryption operations. Secondly, compares between the most popular algorithms RSA , El-Gamal and Elliptic Curve (ECC). Our comparison is based on key size length that affects the running time. Finally, we conclude our survey by focusing on the different outcomes between the RSA and Elliptic curve algorithms.

Commit messages are essential for understanding software evolution and maintaining traceability of projects; however, their quality varies across repositories. Recent Large Language Models provide a promising path to automate this task by generating concise context-sensitive commit messages directly from code diffs. This paper provides a comparative study of three paradigms of large language models: zero-shot prompting, retrieval-augmented generation, and fine-tuning, using the large-scale CommitBench dataset that spans six programming languages. We assess the performance of the models with automatic metrics, namely BLEU, ROUGE-L, METEOR, and Adequacy, and a human assessment of 100 commits. In the latter, experienced developers rated each generated commit message for Adequacy and Fluency on a five-point Likert scale. The results show that fine-tuning and domain adaptation yield models that perform consistently better than general-purpose baselines across all evaluation metrics, thus generating commit messages with higher semantic adequacy and clearer phrasing than zero-shot approaches. The correlation analysis suggests that the Adequacy and BLEU scores are closer to human judgment, while ROUGE-L and METEOR tend to underestimate the quality in cases where the models generate stylistically diverse or paraphrased outputs. Finally, the study outlines a conceptual integration pathway for incorporating such models into software development workflows, emphasizing a human-in-the-loop approach for quality assurance.