Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior chara...Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.展开更多
全比较计算数据分发策略是提高分布式集群系统整体计算性能的关键。针对现有数据分发策略存在的计算负载不均衡、数据不能完全本地化、存储空间浪费和计算速度慢等弊端,在满足数据完全本地化的前提下以负载均衡、最优化存储作为优化目标...全比较计算数据分发策略是提高分布式集群系统整体计算性能的关键。针对现有数据分发策略存在的计算负载不均衡、数据不能完全本地化、存储空间浪费和计算速度慢等弊端,在满足数据完全本地化的前提下以负载均衡、最优化存储作为优化目标,结合优化的粒子群算法提出了数据分发模型(Data Distribution Based on Particle Swarm Optimization,DDBPSO)。DDBPSO模型分别以任务扰动、交换任务的方式对粒子进化规则进行了优化,有效避免了算法陷入局部最优。通过计算负载、存储占用和数据本地化等实验,结果表明,与开源框架Hadoop的数据分发策略相比,提出的DDBPSO模型与算法具有计算负载均衡、完全的数据本地化、存储空间占用小、计算速度快等优势。展开更多
文摘Intrusion detection is critical to guaranteeing the safety of the data in the network.Even though,since Internet commerce has grown at a breakneck pace,network traffic kinds are rising daily,and network behavior characteristics are becoming increasingly complicated,posing significant hurdles to intrusion detection.The challenges in terms of false positives,false negatives,low detection accuracy,high running time,adversarial attacks,uncertain attacks,etc.lead to insecure Intrusion Detection System(IDS).To offset the existing challenge,the work has developed a secure Data Mining Intrusion detection system(DataMIDS)framework using Functional Perturbation(FP)feature selection and Bengio Nesterov Momentum-based Tuned Generative Adversarial Network(BNM-tGAN)attack detection technique.The data mining-based framework provides shallow learning of features and emphasizes feature engineering as well as selection.Initially,the IDS data are analyzed for missing values based on the Marginal Likelihood Fisher Information Matrix technique(MLFIMT)that identifies the relationship among the missing values and attack classes.Based on the analysis,the missing values are classified as Missing Completely at Random(MCAR),Missing at random(MAR),Missing Not at Random(MNAR),and handled according to the types.Thereafter,categorical features are handled followed by feature scaling using Absolute Median Division based Robust Scalar(AMDRS)and the Handling of the imbalanced dataset.The selection of relevant features is initiated using FP that uses‘3’Feature Selection(FS)techniques i.e.,Inverse Chi Square based Flamingo Search(ICS-FSO)wrapper method,Hyperparameter Tuned Threshold based Decision Tree(HpTT-DT)embedded method,and Xavier Normal Distribution based Relief(XavND-Relief)filter method.Finally,the selected features are trained and tested for detecting attacks using BNM-tGAN.The Experimental analysis demonstrates that the introduced DataMIDS framework produces an accurate diagnosis about the attack with low computation time.The work avoids false alarm rate of attacks and remains to be relatively robust against malicious attacks as compared to existing methods.
文摘全比较计算数据分发策略是提高分布式集群系统整体计算性能的关键。针对现有数据分发策略存在的计算负载不均衡、数据不能完全本地化、存储空间浪费和计算速度慢等弊端,在满足数据完全本地化的前提下以负载均衡、最优化存储作为优化目标,结合优化的粒子群算法提出了数据分发模型(Data Distribution Based on Particle Swarm Optimization,DDBPSO)。DDBPSO模型分别以任务扰动、交换任务的方式对粒子进化规则进行了优化,有效避免了算法陷入局部最优。通过计算负载、存储占用和数据本地化等实验,结果表明,与开源框架Hadoop的数据分发策略相比,提出的DDBPSO模型与算法具有计算负载均衡、完全的数据本地化、存储空间占用小、计算速度快等优势。
