Deep Learning-Based Invasion Detection System Enhancing Wireless Sensor Network Security

Wireless sensor networks (WSNs) are vital components of the Internet of Things (IoT), enabling data collection across diverse domains. However, their vulnerabilities expose them to security threats, necessitating robust intrusion detection system (IDS). This project combines convolutional neural networks (CNNs) and long short-term memory (LSTM) networks to enhance WSN security. Traditional methods struggle with evolving threats and false alarms. Our hybrid LSTM + CNN model captures spatial and temporal features, enabling precise intrusion detection. The work develops a real-time model, leveraging CNNs for spatial patterns and LSTMs for temporal dependencies. Comparative analysis demonstrates superior performance. In addition, the work implements an API that offers real-time notifications for prompt reaction and allows for simple integration into WSNs. Our research reduces false alarms and enhances threat detection, contributing to WSN security and IoT reliability. This project signifies a promising stride toward safeguarding the integrity and confidentiality of wireless sensor networks, instilling confidence in the deployment of IoT applications across diverse domains.