Cross-Domain Cybersecurity: Integrated Intrusion Detection Framework
Vishnu Kurnala, Hema Sriya Kanigolla, Mojesh Manda, Anudeep Meda · 2024
This study presents a cross-domain cybersecurity strategy that includes a sophisticated intrusion detection system (IDS) positioned strategically at the default gateway of Healthcare Systems and Industrial Internet of Things (IIoT) infrastructures. The suggested model combines a Convolutional Neural Network (CNN) with a Gated Recurrent Unit (GRU) architecture and incorporates a simulated XGBoost layer to improve predicting accuracy. The CNN-GRU model is designed to extract intricate spatial and temporal characteristics. The CNN component focuses on collecting local patterns, while the GRU component is responsible for addressing sequential dependencies. The outputs are merged with the original input features to create a comprehensive collection of features. This set is then passed through dense layers that simulate the capabilities of XGBoost for the final prediction. This ensemble methodology utilizes deep learning and gradient boosting to enhance accuracy and resilience in classification tasks. The framework prioritizes the processing of data in real-time, guaranteeing the integrity of the system and enabling swift detection of threats. The effectiveness of the model can be measured by its packet processing rate, which demonstrates its performance under different workloads. Our Intrusion Detection System (IDS) establishes a higher benchmark for proactive and dynamic defense, offering strong safeguarding capabilities for healthcare, IIoT, and general networks. It continuously adjusts to changing cyber threats in order to ensure the security of key infrastructure.