Analysis of Most Significant Bit (MSB) Steganography Method in an IoT-Based Keyboard Learning System

Abstract

The advancement of digital technology has driven the development of Internet of Things (IoT)-based learning systems to enhance efficiency and interactivity in the teaching and learning process.  IoT implementations is the development of a sensor-based keyboard learning system. This system is designed to help users improve their typing skills through monitoring parameters such as typing speed, keypress patterns, and usage duration. This data becomes an important asset that requires protection due to its connection to user privacy and personal performance evaluation. In IoT-based systems, data is typically transmitted through networks that are potentially vulnerable to security threats, such as eavesdropping, modification, or theft of data or information. The development of this system is carried out in a sensor-based keyboard learning application with IoT integration, which is equipped with data security features using steganography techniques. Steganography is a technique used to hide data or information within a digital medium so that it cannot be accessed by others. The method used is the  Most Significant Bit (MSB), a simple steganography algorithm that utilizes significant bits from image files to embed secret messages. This research aims to analyze the effectiveness of the MSB method in protecting user data stored and transmitted through IoT system. The implementation is carried out on a sensor-based keyboard learning system, where the data collected from the sensors is used to track typing speed, keypress patterns, and typing duration. This data is encrypted using MSB steganography and embedded into a cover image before being transmitted through the IoT network.