Unlocking Key Lengths: A Deep Learning Approach to Vigenere Ciphers

In this article, we explore a new approach to determining the length of a Vigenère key using artificial neural networks (ANNs). The traditional methods for calculating key lengths, such as the index of coincidence and the twist algorithm, have limitations when dealing with shorter keys. Our proposed ANN-based method can handle shorter keys without sacrificing accuracy.
We begin by explaining the context of the project, which involved downloading approximately 5,500 English text files from the Project Gutenberg website and systematically parsing and cleaning them to create non-overlapping texts of length 200-500. Random keywords were then encrypted with each length to create a dataset for training the ANN.
Next, we describe the ANN architecture used in our approach. The neural network consists of multiple layers, each composed of recurrent units and fully connected layers. We use a combination of English words and random strings as input to the ANN.
The article then delves into the results of our experiments, which show that our ANN-based approach outperforms traditional methods when dealing with shorter keys. In particular, for key lengths between 200 and 499, our method provides a higher accuracy rate than the twist algorithm and index of coincidence.
To further explain our findings, we use analogies to help readers understand complex concepts. For instance, we compare the traditional methods to a "brute force" approach, where one tries every possible combination until the correct key is found. In contrast, our ANN-based approach is like using a "map" to navigate through the possible combinations, making it more efficient and accurate.
Finally, the article concludes by highlighting the potential implications of our research, including the possibility of applying the ANN approach to other cryptographic methods. We also note that future work may involve improving the accuracy of the ANN-based method or exploring other approaches to key length determination.
In summary, this article presents a novel approach to determining Vigenère key lengths using artificial neural networks. By leveraging the power of machine learning, we can improve the accuracy and efficiency of cryptographic methods, making them more secure and practical for everyday use.