Shaikh Jamshed Ali, Wang Chengliang, Sima Muhammad Wajeeh Us, Arshad Muhammad, Owais Muhammad, Hassan Dina S M, Alkanhel Reem, Muthanna Mohammed Saleh Ali
Department of Computer Science and Technology, Chongqing University, Chongqing, China.
Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.
Front Med (Lausanne). 2025 Apr 8;12:1524286. doi: 10.3389/fmed.2025.1524286. eCollection 2025.
The Internet of Medical Things (IoMT) is transforming healthcare by enabling continuous remote patient monitoring, diagnostics, and personalized therapies. However, the widespread deployment of these devices introduces significant security vulnerabilities due to limited resources and inadequate network protocols. Intrusions within IoMT networks can compromise patient privacy, disrupt critical medical services, and jeopardize patient safety. To address these challenges, we propose HCLR-IDS, an advanced Intrusion Detection System (IDS) specifically designed for IoMT networks. The system integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and Reinforcement Learning (RL) techniques, namely Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), to enhance the detection of evolving threats. The methodology begins with Enhanced Mutual Information Feature Selection (MIFS) to preprocess the CICIoMT2024 dataset, selecting the most relevant features while reducing noise and computational complexity. These selected features are then passed through a hybrid CNN-LSTM architecture. The CNN captures spatial patterns in network traffic, while the LSTM identifies temporal patterns. This dual feature extraction approach enables the system to effectively detect both static and dynamic characteristics of IoMT data. After feature extraction, the model incorporates DQN and PPO for decision-making. DQN optimizes actions based on Q-values, enhancing detection rewards, while PPO ensures stability in dynamic environments through a clipping mechanism. This combination of adaptive Q-learning and stable policy optimization significantly improves system robustness, ensuring effective real-time intrusion detection. The model demonstrates exceptional performance with binary classification accuracy of 0.9958, outperforming traditional IDS models. Additionally, it performs effectively in multi-class classification across 18 classes, achieving an accuracy of 0.7773. These results highlight that HCLR-IDS offers a reliable and efficient solution for securing IoMT healthcare systems.
医疗物联网（IoMT）正在通过实现持续的远程患者监测、诊断和个性化治疗来改变医疗保健。然而，由于资源有限和网络协议不完善，这些设备的广泛部署带来了重大的安全漏洞。IoMT网络中的入侵可能会危及患者隐私、扰乱关键医疗服务并危及患者安全。为应对这些挑战，我们提出了HCLR-IDS，这是一种专门为IoMT网络设计的先进入侵检测系统（IDS）。该系统集成了卷积神经网络（CNN）、长短期记忆（LSTM）网络和强化学习（RL）技术，即深度Q网络（DQN）和近端策略优化（PPO），以增强对不断演变的威胁的检测。该方法首先通过增强互信息特征选择（MIFS）对CICIoMT2024数据集进行预处理，选择最相关的特征，同时减少噪声和计算复杂性。然后将这些选定的特征通过混合CNN-LSTM架构。CNN捕获网络流量中的空间模式，而LSTM识别时间模式。这种双重特征提取方法使系统能够有效检测IoMT数据的静态和动态特征。特征提取后，模型结合DQN和PPO进行决策。DQN基于Q值优化动作，增强检测奖励，而PPO通过裁剪机制确保动态环境中的稳定性。这种自适应Q学习和稳定策略优化的结合显著提高了系统的鲁棒性，确保了有效的实时入侵检测。该模型在二分类准确率为0.9958时表现出色，优于传统的IDS模型。此外，它在18类的多分类中也能有效运行，准确率达到0.7773。这些结果表明，HCLR-IDS为保护IoMT医疗系统提供了可靠且高效的解决方案。
