A team from the ICFAI Foundation for Higher Education and collaborating universities introduces SADDBN-AMOA: they normalize IoHT data with Z-score, select features via slime mould optimization, classify intrusions using a deep belief network, and fine-tune hyperparameters with an improved Harris Hawk algorithm, achieving 98.71% accuracy against IoT healthcare cyber threats.
Key points
Z-score normalization standardizes 50 raw IoHT telemetry features to zero mean and unit variance, improving model stability.
Slime mould optimization reduces dimensionality by selecting a compact feature subset that maximizes classification accuracy and minimizes model complexity.
Deep belief network classification, fine-tuned via improved Harris Hawk optimization, achieves 98.71% accuracy on an IoT healthcare security dataset.
Why it matters:
This integrated AI-driven intrusion detection pipeline substantially elevates security for critical healthcare IoT networks, reducing risk of patient data breaches.
Q&A
What is the Internet of Health Things (IoHT)?
How does slime mould optimization select features?
What distinguishes a deep belief network from standard neural networks?
Why is hyperparameter tuning critical for deep learning intrusion detection?
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Academy
Internet of Health Things (IoHT)
Definition and Scope
The Internet of Health Things (IoHT) encompasses interconnected medical devices, sensors, and applications that collect and exchange patient data via the internet. These systems include wearable health monitors (e.g., heart rate, glucose sensors), implantable devices, and remote diagnostic tools. By enabling continuous monitoring and real-time data analysis, IoHT supports personalized healthcare, chronic disease management, and telemedicine.
Key Components
- Sensors and Devices: Wearables (fitness trackers, smartwatches), implantables (pacemakers), and environmental sensors.
- Connectivity: Wireless protocols (Bluetooth, Wi-Fi, 5G) and networking infrastructure.
- Data Processing: Edge computing nodes or cloud servers for analysis, storage, and decision support.
- Applications: Remote patient monitoring, telehealth consultations, predictive analytics for disease prevention.
Benefits and Challenges
IoHT brings enhanced patient engagement, early detection of health anomalies, and efficient resource allocation in healthcare systems. However, it also introduces cybersecurity risks, data privacy concerns, and interoperability challenges between diverse devices and platforms. Ensuring strong encryption, secure authentication, and intrusion detection mechanisms is essential to protect sensitive health data and maintain patient safety.
Deep Belief Networks for Intrusion Detection
Model Structure
A deep belief network (DBN) is a hierarchical generative model composed of stacked restricted Boltzmann machines (RBMs). Each RBM learns to represent underlying patterns in data through unsupervised pretraining. After stacking multiple RBMs, a supervised classification layer (e.g., softmax) is added for anomaly detection or intrusion classification tasks.
Training Phases
- Unsupervised Pretraining: Each RBM layer is trained independently using contrastive divergence to maximize data likelihood, capturing feature hierarchies.
- Supervised Fine-Tuning: The entire network undergoes backpropagation training, adjusting weights to minimize classification loss on labeled intrusion data.
Integration in IoHT Security
In IoHT intrusion detection, a DBN processes standardized sensor and network traffic features, extracting deep representations that distinguish normal behavior from malicious activity. When combined with metaheuristic optimizations—such as slime mould for feature selection and Harris Hawk for hyperparameter tuning—the DBN attains high detection accuracy and low false-positive rates, even on resource-constrained healthcare devices.
Practical Considerations
- Data Preprocessing: Standardize raw telemetry using methods like Z-score normalization.
- Feature Selection: Apply metaheuristic algorithms to reduce dimensionality and focus on the most informative indicators.
- Model Deployment: Optimize model size and inference speed for edge computing on IoHT gateways.
- Continuous Learning: Update the DBN with new threat signatures and anomaly patterns to adapt to evolving cyber threats.
By understanding IoHT architecture and leveraging deep belief networks, healthcare practitioners and engineers can design robust intrusion detection systems that secure patient data streams and ensure reliable operation of critical medical devices in smart city environments.
