IoTShield Intelligent Intrusion Detection System for IoT Networks

With the rapid proliferation of IoT devices, ensuring network security has become a critical challenge. Traditional security measures often fail to detect sophisticated cyber threats targeting IoT networks. This project, IoTShield: Intelligent Intrusion Detection System for IoT Networks, proposes an advanced intrusion detection mechanism leveraging machine learning to classify network traffic as either malicious or normal. IoTShield is trained on the TON_IoT dataset and integrates Support Vector Machines (SVM) and Artificial Neural Networks (ANN) to enhance detection accuracy. The system allows users to upload IoT network traffic data, processes it using pre-trained models, and generates detailed classification reports and accuracy metrics. Additionally, feature selection techniques like Symmetric Uncertainty are applied to optimize model performance and reduce false positives. Designed for scalability, IoTShield can be deployed in enterprise IoT environments to monitor thousands of connected devices in real-time. By providing automated intrusion detection and visual analytics, the system strengthens IoT security, mitigating potential threats before they impact critical infrastructure.