SECURE DATA TRANSMISSION IN IOTs APPLICATION

Abstract

The Internet of Things (IoT) has become one of the most important modern technologies due to its ability to interconnect smart devices and enable real-time data exchange across heterogeneous environments. IoT systems are widely deployed in applications such as smart homes, industrial automation, healthcare monitoring, environmental sensing, and intelligent transportation systems. However, the increasing number of connected devices has introduced major challenges related to secure data transmission, confidentiality, integrity, and resistance to cyberattacks. The main objective of this thesis is to enhance the security of IoT communications using a hybrid cybersecurity approach combining cryptographic mechanisms and artificial intelligence techniques. The proposed architecture integrates Elliptic Curve Cryptography (ECC) for secure key exchange, Advanced Encryption Standard (AES) for lightweight data encryption, Cyclic Redundancy Check (CRC) for integrity verification, MQTT communication for lightweight IoT data transmission, and a Long Short-Term Memory (LSTM) deep learning model for intelligent intrusion detection. A complete simulation environment was developed using MATLAB and Simulink in order to reproduce realistic IoT communication scenarios. The proposed system was evaluated under different cyberattack conditions including Man-in-the-Middle (MITM) attacks, replay attacks, data injection attacks, and Denial of Service (DoS) attacks. The obtained results demonstrate that the integration of AES encryption, ECC-based key protection, CRC integrity verification, and LSTM-based intrusion detection significantly improves the reliability and security of IoT data transmission while maintaining acceptable communication latency and system efficiency.