CryptoDNA：受加密劫持预防模型启发的医疗物联网DDoS检测机器学习范式
CryptoDNA: A Machine Learning Paradigm for DDoS Detection in Healthcare IoT, Inspired by crypto jacking prevention Models
AI总结：
提出受加密劫持检测启发的轻量级机器学习框架CryptoDNA，通过行为分析与动态异常检测在医疗物联网中以超96%准确率检测DDoS攻击，兼顾低开销与强可扩展性。
摘要：
物联网和医疗互联网设备在医疗行业的快速融合显著改善了患者护理和医院运营，但同时也带来了巨大的风险。分布式拒绝服务攻击构成了重大威胁，危及运营稳定性和患者安全。本研究提出了CryptoDNA，一种受加密劫持检测方法影响的创新机器学习检测框架，旨在识别和缓解医疗物联网环境中的DDoS攻击。该方法依赖于行为分析，包括异常资源使用和网络活动模式。源自加密劫持启发方法的关键特征包括基于熵的流量分析、设备性能的时间序列监控和动态异常检测。轻量级架构确保了与资源受限物联网设备的互操作性，同时保持了高检测精度。所提出的架构和模型在真实和合成数据集上进行了测试，以证明模型的卓越性能，以最小的计算开销实现了超过96%的准确率。比较分析揭示了其对新兴攻击向量的抵御能力和在多样化设备生态系统中的可扩展性。通过连接加密劫持和DDoS检测的原理，CryptoDNA提供了一个强大、创新的解决方案，以加强医疗物联网环境抵御不断演变的网络威胁，并突出了跨学科方法在关键医疗基础设施自适应网络安全防御机制中的潜力。
英文摘要：
The rapid integration of the Internet of Things (IoT) and Internet of Medical (IoM) devices in the healthcare industry has markedly improved patient care and hospital operations but has concurrently brought substantial risks. Distributed Denial-of-Service (DDoS) attacks present significant dangers, jeopardizing operational stability and patient safety. This study introduces CryptoDNA, an innovative machine learning detection framework influenced by cryptojacking detection methods, designed to identify and alleviate DDoS attacks in healthcare IoT settings. The proposed approach relies on behavioral analytics, including atypical resource usage and network activity patterns. Key features derived from cryptojacking-inspired methodologies include entropy-based analysis of traffic, time-series monitoring of device performance, and dynamic anomaly detection. A lightweight architecture ensures inter-compatibility with resource-constrained IoT devices while maintaining high detection accuracy. The proposed architecture and model were tested in real-world and synthetic datasets to demonstrate the model's superior performance, achieving over 96% accuracy with minimal computational overhead. Comparative analysis reveals its resilience against emerging attack vectors and scalability across diverse device ecosystems. By bridging principles from cryptojacking and DDoS detection, CryptoDNA offers a robust, innovative solution to fortify the healthcare IoT landscape against evolving cyber threats and highlights the potential of interdisciplinary approaches in adaptive cybersecurity defense mechanisms for critical healthcare infrastructures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
