Design and Development of a Machine Learning Model for Detecting Data Hidden Techniques

Data security is very important when sensitive data are transmitted over the Internet. Steganography and steganalysis techniques can solve the problem of copyright, ownership, and detection malicious data. Steganography is to hide secret data without distortion and steganalysis is to detect the presence of hidden data. This study focuses on the design and development of a machine learning model for detecting data hiding techniques such as steganography, encryption-based obfuscation, and covert channel embedding. Several machine learning models including SVM, Random Forest, and CNNs were evaluated. The results demonstrate that machine learning-based steganalysis enhances forensic capabilities and cybersecurity investigations.