Cipherkeyx Algorithm: Cryptographic Block Cipher Algorithm for Secure Machine Learning Authentication

With increasing cyber threats, secure authentication of users in machine learning systems has never been more necessary. In this paper, we develop the CipherKeyX Algorithm: A novel cryptographic block cipher able to reinforce secure authentication mechanisms in machine learning frameworks using an Improved Support Vector Machine (ISVM) for efficient classification. CipherKeyX Algorithm – This is a block cipher encryption mechanism that creates real-time/ dynamic crypto keys for each token transaction. This minimizes the risks of key exposure and replay attacks. Through the application of ISVM, the algorithm is able to accurately predict whether authentication attempts are genuine or fraudulent, using all features extracted from both encryption and log data to take full advantage of its capabilities in high-dimensional data analysis and optimally populating hyperplanes for classification. Our extensive experiments on different datasets show that CipherKeyX Algorithm hits high accuracy in user authentication with minimal complexity for real-time applications. We further experiment with the immunity of the model towards common cyber threats, and our findings are encouragingly positive for unauthorized access. The proposed framework thus paves the way for tackling the sort of challenges associated with secure authentication, which represents one kind of more general cryptographic-machine-learning mash-up problem. CipherKeyX on the other hand utilises a combination of block cipher method along with ISVM which creates an excessive reliable solution for bettering device integrity and safety in many domains such as healthcare, monetary offerings, self-sufficient structures and lots greater.