The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)an...The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.展开更多
目的:基于专利引文网络探索类器官领域的技术发展主路径。方法:本研究通过构建类器官领域的专利引文网络,采用搜索路径连接数算法(search path link count,SPLC)计算遍历权重,对类器官领域开展主路径分析,探索该领域的技术发展轨迹。结...目的:基于专利引文网络探索类器官领域的技术发展主路径。方法:本研究通过构建类器官领域的专利引文网络,采用搜索路径连接数算法(search path link count,SPLC)计算遍历权重,对类器官领域开展主路径分析,探索该领域的技术发展轨迹。结果:类器官领域共有专利申请2 250项,包含专利引文12 722件;专利申请数量逐年增长,技术开发聚焦于疾病模型、药物筛选、细胞培养及器官芯片等方向。主路径分析显示,全局主路径上专利数量最多,有12件,包含1条技术路线,全局关键路径主路径与全局主路径一致;局部前向主路径上有10件专利,包含1条技术路线;这2条技术路线反映出中国类器官领域的技术发展轨迹,中国技术创新聚焦于基于肿瘤类器官技术的疾病机制研究、基于肺癌类器官模型的疾病机制研究、肺癌类器官模型的开发与优化。局部后向主路径上有9件专利,包含2条技术路线,局部关键路径主路径与局部后向主路径一致;这2条技术路线反映出美国类器官领域的技术发展轨迹,技术创新聚焦于胃肠道类器官培养与疾病模型研究、干细胞驱动的器官功能修复技术、细胞移植与器官再生。结论:本研究通过类器官领域的专利主路径分析,识别技术发展轨迹,从情报学角度为类器官研发提供信息支撑。展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2025R97)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘The exponential growth of the Internet of Things(IoT)has introduced significant security challenges,with zero-day attacks emerging as one of the most critical and challenging threats.Traditional Machine Learning(ML)and Deep Learning(DL)techniques have demonstrated promising early detection capabilities.However,their effectiveness is limited when handling the vast volumes of IoT-generated data due to scalability constraints,high computational costs,and the costly time-intensive process of data labeling.To address these challenges,this study proposes a Federated Learning(FL)framework that leverages collaborative and hybrid supervised learning to enhance cyber threat detection in IoT networks.By employing Deep Neural Networks(DNNs)and decentralized model training,the approach reduces computational complexity while improving detection accuracy.The proposed model demonstrates robust performance,achieving accuracies of 94.34%,99.95%,and 87.94%on the publicly available kitsune,Bot-IoT,and UNSW-NB15 datasets,respectively.Furthermore,its ability to detect zero-day attacks is validated through evaluations on two additional benchmark datasets,TON-IoT and IoT-23,using a Deep Federated Learning(DFL)framework,underscoring the generalization and effectiveness of the model in heterogeneous and decentralized IoT environments.Experimental results demonstrate superior performance over existing methods,establishing the proposed framework as an efficient and scalable solution for IoT security.
