The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT n...The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.展开更多
针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现...针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。展开更多
文摘The rapid growth of IoT networks necessitates efficient Intrusion Detection Systems(IDS)capable of addressing dynamic security threats under constrained resource environments.This paper proposes a hybrid IDS for IoT networks,integrating Support Vector Machine(SVM)and Genetic Algorithm(GA)for feature selection and parameter optimization.The GA reduces the feature set from 41 to 7,achieving a 30%reduction in overhead while maintaining an attack detection rate of 98.79%.Evaluated on the NSL-KDD dataset,the system demonstrates an accuracy of 97.36%,a recall of 98.42%,and an F1-score of 96.67%,with a low false positive rate of 1.5%.Additionally,it effectively detects critical User-to-Root(U2R)attacks at a rate of 96.2%and Remote-to-Local(R2L)attacks at 95.8%.Performance tests validate the system’s scalability for networks with up to 2000 nodes,with detection latencies of 120 ms at 65%CPU utilization in small-scale deployments and 250 ms at 85%CPU utilization in large-scale scenarios.Parameter sensitivity analysis enhances model robustness,while false positive examination aids in reducing administrative overhead for practical deployment.This IDS offers an effective,scalable,and resource-efficient solution for real-world IoT system security,outperforming traditional approaches.
文摘针对燃煤机组锅炉主再热汽温控制中存在的滞后性、多变量耦合及动态工况适应难题,文章提出一种融合数字孪生技术与最小二乘支持向量机(Least Squares Support Vector Machine,LS-SVM)的汽温寻优方法。通过构建锅炉三维数字孪生模型实现设备状态实时映射,结合LS-SVM建立多变量动态预测模型,并引入多目标微分进化算法(MODE)进行参数优化。实际应用表明,该方法使主汽温波动范围从±7℃缩小至±2.5℃,再热汽温预测误差稳定在±1.5℃以内,年节约燃煤成本超400万元,为火电机组深度调峰与能效提升提供技术支撑。
