In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing researc...In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods.展开更多
Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural N...Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.展开更多
目的：观察首乌丹参方（Gold Theragran Salvia Mihiorrhiza Prescription，GTSMP）对大鼠缺血再灌注损伤心肌梗死范围及血中白介素-1β（IL-1β）、肿瘤坏死因子（TNF-α）的影响。方法：实验共设7个组：假手术组、缺血再灌注组（L／R...目的：观察首乌丹参方（Gold Theragran Salvia Mihiorrhiza Prescription，GTSMP）对大鼠缺血再灌注损伤心肌梗死范围及血中白介素-1β（IL-1β）、肿瘤坏死因子（TNF-α）的影响。方法：实验共设7个组：假手术组、缺血再灌注组（L／R组）和首乌丹参方高、中、低剂量组，对照药组（复方丹参滴丸、消心痛）。采用结扎大鼠冠状动脉前降支30min／开放120min建立心肌缺血再灌注损伤（L／R）模型，通过首乌丹参方预处理，观察L／R大鼠心肌梗死范围以及血中的IL-1β、TNF-α含量的变化。结果：首乌丹参方高、中两个剂量组梗塞程度明显减轻，梗塞区重占全心脏及左心室的百分比与模型组比较均有显著性差异（P〈0．01．0．001），以首乌丹参方中剂量组为优；首乌丹参方给药后血中TNF-α、IL-1β有不同程度的下降，其中以高、中剂量组为优（P〈0．01）。结论：首乌丹参方能够减轻TNF-α、IL-1β对缺血再灌注心肌的损伤。展开更多
基金Supported by the National Natural Science Foundation of China(11871452,12071052the Natural Science Foundation of Henan(202300410338)the Nanhu Scholar Program for Young Scholars of XYNU。
文摘In Wireless Sensor Networks(WSN),attacks mostly aim in limiting or eliminating the capability of the network to do its normal function.Detecting this misbehaviour is a demanding issue.And so far the prevailing research methods show poor performance.AQN3 centred efficient Intrusion Detection Systems(IDS)is proposed in WSN to ameliorate the performance.The proposed system encompasses Data Gathering(DG)in WSN as well as Intrusion Detection(ID)phases.In DG,the Sensor Nodes(SN)is formed as clusters in the WSN and the Distance-based Fruit Fly Fuzzy c-means(DFFF)algorithm chooses the Cluster Head(CH).Then,the data is amassed by the discovered path.Next,it is tested with the trained IDS.The IDS encompasses‘3’steps:pre-processing,matrix reduction,and classification.In pre-processing,the data is organized in a clear format.Then,attributes are presented on the matrix format and the ELDA(entropybased linear discriminant analysis)lessens the matrix values.Next,the output as of the matrix reduction is inputted to the QN3 classifier,which classifies the denial-of-services(DoS),Remotes to Local(R2L),Users to Root(U2R),and probes into attacked or Normal data.In an experimental estimation,the proposed algorithm’s performance is contrasted with the prevailing algorithms.The proposed work attains an enhanced outcome than the prevailing methods.
文摘Intrusion Detection Systems (IDS) are pivotal in safeguarding computer networks from malicious activities. This study presents a novel approach by proposing a Hybrid Dense Neural Network-Radial Basis Function Neural Network (DNN-RBFNN) architecture to enhance the accuracy and efficiency of IDS. The hybrid model synergizes the strengths of both dense learning and radial basis function networks, aiming to address the limitations of traditional IDS techniques in classifying packets that could result in Remote-to-local (R2L), Denial of Service (Dos), and User-to-root (U2R) intrusions.
文摘目的：观察首乌丹参方（Gold Theragran Salvia Mihiorrhiza Prescription，GTSMP）对大鼠缺血再灌注损伤心肌梗死范围及血中白介素-1β（IL-1β）、肿瘤坏死因子（TNF-α）的影响。方法：实验共设7个组：假手术组、缺血再灌注组（L／R组）和首乌丹参方高、中、低剂量组，对照药组（复方丹参滴丸、消心痛）。采用结扎大鼠冠状动脉前降支30min／开放120min建立心肌缺血再灌注损伤（L／R）模型，通过首乌丹参方预处理，观察L／R大鼠心肌梗死范围以及血中的IL-1β、TNF-α含量的变化。结果：首乌丹参方高、中两个剂量组梗塞程度明显减轻，梗塞区重占全心脏及左心室的百分比与模型组比较均有显著性差异（P〈0．01．0．001），以首乌丹参方中剂量组为优；首乌丹参方给药后血中TNF-α、IL-1β有不同程度的下降，其中以高、中剂量组为优（P〈0．01）。结论：首乌丹参方能够减轻TNF-α、IL-1β对缺血再灌注心肌的损伤。
