A Cyber Security Model Using Gaussian Noise for Text Encryption and Decryption Algorithm

Cybersecurity has become a critical issue in protecting personal information for all internet users, so the need to protect such information through different techniques has become necessary. One of these techniques is cryptography, which is used to protect data transferred over the internet by converting readable messages into unreadable messages. This paper introduces an innovative model for encryption and decryption utilizing Gaussian noise incorporation and denoising, exploiting the statistical characteristics of Gaussian distributions as the basis for encryption. The model proposed in this study encodes text through the transformation of characters into their corresponding ASCII values, followed by the addition of Gaussian noise characterized by specific mean (μ) and standard deviation (σ) parameters. The decryption process involves accurately subtracting the identical Gaussian noise, consequently retrieving the original text. The effectiveness of this technique heavily depends on maintaining the confidentiality of the Gaussian noise parameters. The results of our studies showcase specific decryption with a confined computational burden, rendering it suitable for applications with low processing capabilities, together with Internet of Things (IoT) gadgets and embedded systems. A side-by-side comparison of this approach with traditional encryption methods such as AES, RSA, and lightweight block ciphers is conducted, highlighting its advantages and possible application in resource-sensitive contexts. Analysis reveals that while the Gaussian noise-focused approach is straightforward and effective, more cryptographic verification is required to provide a strong defense against more complex cyberattacks. This test opens up the possibility of investigating noise-based fully cryptographic frameworks and their application in establishing consistent communication channels.