FAM(Fuzzy Associative Memory) Network Model, FAM Adaptive Learning Algorithm and Principal of FAM Inference Machine are introduced, and successfully application to ″New Generation Expert System for Earthquake Predict...FAM(Fuzzy Associative Memory) Network Model, FAM Adaptive Learning Algorithm and Principal of FAM Inference Machine are introduced, and successfully application to ″New Generation Expert System for Earthquake Prediction″ (NGESEP). This system has good function for knowledge learning without disadvantages of neural network, which the learned knowledge implied in network is difficult to be understood or interpreted by expert system.展开更多
Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervise...Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.展开更多
Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,a...Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.展开更多
结合神经网络与模糊系统相融合的3种形式,提出了基于"前层神经网络+后层模糊系统结构"的入侵检测系统模型。通过SQL server 2008软件进行数据预处理,采用一种基于主成分分析的降维方法进行数据降维,改进的LMBP算法对神经网络...结合神经网络与模糊系统相融合的3种形式,提出了基于"前层神经网络+后层模糊系统结构"的入侵检测系统模型。通过SQL server 2008软件进行数据预处理,采用一种基于主成分分析的降维方法进行数据降维,改进的LMBP算法对神经网络进行训练和判别,完成对网络入侵KDD CUP 99数据集的验证性实验。实验结果表明,系统检测效果良好,判别准确率高,为实现高效准确的入侵检测提供了一种有效的方法。展开更多
文摘FAM(Fuzzy Associative Memory) Network Model, FAM Adaptive Learning Algorithm and Principal of FAM Inference Machine are introduced, and successfully application to ″New Generation Expert System for Earthquake Prediction″ (NGESEP). This system has good function for knowledge learning without disadvantages of neural network, which the learned knowledge implied in network is difficult to be understood or interpreted by expert system.
文摘Due to the increasing number of cyber-attacks,the necessity to develop efficient intrusion detection systems(IDS)is more imperative than ever.In IDS research,the most effectively used methodology is based on supervised Neural Networks(NN)and unsupervised clustering,but there are few works dedicated to their hybridization with metaheuristic algorithms.As intrusion detection data usually contains several features,it is essential to select the best ones appropriately.Linear Discriminant Analysis(LDA)and t-statistic are considered as efficient conventional techniques to select the best features,but they have been little exploited in IDS design.Thus,the research proposed in this paper can be summarized as follows.a)The proposed approach aims to use hybridized unsupervised and hybridized supervised detection processes of all the attack categories in the CICIDS2017 Dataset.Nevertheless,owing to the large size of the CICIDS2017 Dataset,only 25%of the data was used.b)As a feature selection method,the LDAperformancemeasure is chosen and combinedwith the t-statistic.c)For intrusion detection,unsupervised Fuzzy C-means(FCM)clustering and supervised Back-propagation NN are adopted.d)In addition and in order to enhance the suggested classifiers,FCM and NN are hybridized with the seven most known metaheuristic algorithms,including Genetic Algorithm(GA),Particle Swarm Optimization(PSO),Differential Evolution(DE),Cultural Algorithm(CA),Harmony Search(HS),Ant-Lion Optimizer(ALO)and Black Hole(BH)Algorithm.Performance metrics extracted from confusion matrices,such as accuracy,precision,sensitivity and F1-score are exploited.The experimental result for the proposed intrusion detection,based on training and test CICIDS2017 datasets,indicated that PSO,GA and ALO-based NNs can achieve promising results.PSO-NN produces a tested accuracy,global sensitivity and F1-score of 99.97%,99.95%and 99.96%,respectively,outperforming performance concluded in several related works.Furthermore,the best-proposed approaches are valued in the most recent intrusion detection datasets:CSE-CICIDS2018 and LUFlow2020.The evaluation fallouts consolidate the previous results and confirm their correctness.
文摘Wireless sensor networks(WSN)gather information and sense information samples in a certain region and communicate these readings to a base station(BS).Energy efficiency is considered a major design issue in the WSNs,and can be addressed using clustering and routing techniques.Information is sent from the source to the BS via routing procedures.However,these routing protocols must ensure that packets are delivered securely,guaranteeing that neither adversaries nor unauthentic individuals have access to the sent information.Secure data transfer is intended to protect the data from illegal access,damage,or disruption.Thus,in the proposed model,secure data transmission is developed in an energy-effective manner.A low-energy adaptive clustering hierarchy(LEACH)is developed to efficiently transfer the data.For the intrusion detection systems(IDS),Fuzzy logic and artificial neural networks(ANNs)are proposed.Initially,the nodes were randomly placed in the network and initialized to gather information.To ensure fair energy dissipation between the nodes,LEACH randomly chooses cluster heads(CHs)and allocates this role to the various nodes based on a round-robin management mechanism.The intrusion-detection procedure was then utilized to determine whether intruders were present in the network.Within the WSN,a Fuzzy interference rule was utilized to distinguish the malicious nodes from legal nodes.Subsequently,an ANN was employed to distinguish the harmful nodes from suspicious nodes.The effectiveness of the proposed approach was validated using metrics that attained 97%accuracy,97%specificity,and 97%sensitivity of 95%.Thus,it was proved that the LEACH and Fuzzy-based IDS approaches are the best choices for securing data transmission in an energy-efficient manner.
文摘结合神经网络与模糊系统相融合的3种形式,提出了基于"前层神经网络+后层模糊系统结构"的入侵检测系统模型。通过SQL server 2008软件进行数据预处理,采用一种基于主成分分析的降维方法进行数据降维,改进的LMBP算法对神经网络进行训练和判别,完成对网络入侵KDD CUP 99数据集的验证性实验。实验结果表明,系统检测效果良好,判别准确率高,为实现高效准确的入侵检测提供了一种有效的方法。
