The integration of Internet of Things (IoT) and Internet of Medical (IoM) devices has transformed healthcare systems, enabling real-time monitoring, remote diagnostics, and data-driven decision-making.
However, these advancements come with significant cybersecurity risks, particularly Distributed Denial-of-Service (DDoS) attacks.
These attacks, which disrupt operations by overwhelming devices with illegitimate requests, pose severe threats to patient safety and operational stability.
In 2024 alone, healthcare systems faced an average of 29.3 DDoS attacks per day, underscoring the urgency for robust security solutions.
Traditional DDoS detection methods often rely on resource-intensive algorithms unsuitable for the constrained computational capabilities of IoT devices.
Moreover, these methods struggle to adapt to evolving attack vectors.
Recognizing these challenges, researchers have developed CryptoDNA a novel machine learning-based framework inspired by cryptojacking detection methodologies to address the unique security needs of healthcare IoT environments.
An Innovative Detection Framework
CryptoDNA leverages behavioral analytics to monitor device performance and detect anomalies indicative of DDoS attacks.
Drawing inspiration from cryptojacking detection techniques, the framework incorporates features such as entropy-based traffic analysis, time-series monitoring of device performance, and lightweight architecture tailored for resource-constrained IoT devices.
This approach ensures high detection accuracy while maintaining minimal computational overhead.
The framework consists of four layers:
- Data Acquisition Layer: Collects real-time data streams from IoT devices, including network traffic logs and resource usage metrics.
- Feature Extraction Layer: Implements entropy-based and statistical analyses to identify anomalies in device behavior.
- Machine Learning Layer: Utilizes lightweight models like Random Forest classifiers for real-time detection.
- Detection and Response Layer: Flags potential threats and dynamically adjusts thresholds based on device usage.
Performance Evaluation
CryptoDNA was tested using real-world and synthetic datasets to simulate diverse DDoS attack scenarios.
The framework achieved a detection accuracy of 96.8%, outperforming existing methods in terms of precision and scalability.
Its lightweight architecture reduced model size by 35% and inference latency by 40%, making it suitable for deployment on edge devices with limited computational power.
CryptoDNA represents a breakthrough in securing healthcare IoT infrastructures against cyber threats.
By adapting cryptojacking-inspired techniques to DDoS detection, it bridges gaps in existing solutions while addressing the unique constraints of healthcare environments.
However, its reliance on labeled training data highlights the need for future research into semi-supervised or unsupervised learning approaches.
As cyberattacks on healthcare systems continue to evolve, frameworks like CryptoDNA offer a promising path forward.
By combining advanced machine learning techniques with domain-specific insights, this innovative solution sets a new standard for adaptive cybersecurity defense mechanisms in critical healthcare infrastructures.
