The adaptation of data and communication security techniques in the context of the Internet of Things

With the continuous expansion of IoT applications, ensuring robust data security has become crucial, particularly in sensitive domains such as healthcare, smart cities, and industrial automation. Traditional security mechanisms are often insuffcient due to the heterogeneity and constraints of IoT devices. This research investigates how conventional cryptographic techniques can be optimized for lightweight and effcient implementation in IoT systems. By leveraging homomorphic encryption and adaptive security measures, this work aims to provide a practical and scalable security solution tailored to the constraints of IoT environments. The Internet of Things (IoT) presents vast opportunities but also significant security challenges due to its interconnected nature. This thesis explores the adaptation of data protection and secure communication techniques to the IoT context. It introduces a novel encryption scheme, Multi-Key Embedded Encryption (MKEE), combined with the Modular Multiplicative Inverse (MMI) technique, achieving a 94% reduction in encrypted data storage. Furthermore, this research presents a deep learning-based model for Tuberculosis detection, demonstrating high accuracy using advanced convolutional neural networks, reaching 99.66% for training and 99.78% for validation. The experimental results validate the proposed methodologies through real-world case studies, showing significant improvements in both storage effciency and security robustness. The deep learning model developed for medical image analysis shows promise in enhancing early disease detection. Ultimately, this thesis contributes valuable insights into securing IoT ecosystems while maintaining system performance and usability