对VB IC B JT模型用于Ⅲ-V族化合物HBT器件建模的可行性进行了讨论和借鉴,结合UCSD HBT模型优点,提出一个新的可精确用于单异质结InGaP/GaAsHBT模型,并用于该类器件建模。测量和模型仿真I-V特性及多偏置条件下多频率点S参数对比结果表明...对VB IC B JT模型用于Ⅲ-V族化合物HBT器件建模的可行性进行了讨论和借鉴,结合UCSD HBT模型优点,提出一个新的可精确用于单异质结InGaP/GaAsHBT模型,并用于该类器件建模。测量和模型仿真I-V特性及多偏置条件下多频率点S参数对比结果表明,DC^20GHz频率范围内,新模型可对单、多指InGaP/GaAs HBT器件交流小信号特性进行精确表征。运用所建模型准确的预见了一宽带放大器性能。展开更多
In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that eff...In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Na?ve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Na?ve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using “The CAIDA UCSD DDoS Attack 2007 Dataset” and “DARPA 2000 Dataset” and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.展开更多
文摘对VB IC B JT模型用于Ⅲ-V族化合物HBT器件建模的可行性进行了讨论和借鉴,结合UCSD HBT模型优点,提出一个新的可精确用于单异质结InGaP/GaAsHBT模型,并用于该类器件建模。测量和模型仿真I-V特性及多偏置条件下多频率点S参数对比结果表明,DC^20GHz频率范围内,新模型可对单、多指InGaP/GaAs HBT器件交流小信号特性进行精确表征。运用所建模型准确的预见了一宽带放大器性能。
基金The authors would like to extend their gratitude to Department of Graduate StudiesNepal College of Information Technology for its constant support and motivationWe would also like to thank the Journal of Information Security for its feedbacks and reviews
文摘In recent times among the multitude of attacks present in network system, DDoS attacks have emerged to be the attacks with the most devastating effects. The main objective of this paper is to propose a system that effectively detects DDoS attacks appearing in any networked system using the clustering technique of data mining followed by classification. This method uses a Heuristics Clustering Algorithm (HCA) to cluster the available data and Na?ve Bayes (NB) classification to classify the data and detect the attacks created in the system based on some network attributes of the data packet. The clustering algorithm is based in unsupervised learning technique and is sometimes unable to detect some of the attack instances and few normal instances, therefore classification techniques are also used along with clustering to overcome this classification problem and to enhance the accuracy. Na?ve Bayes classifiers are based on very strong independence assumptions with fairly simple construction to derive the conditional probability for each relationship. A series of experiment is performed using “The CAIDA UCSD DDoS Attack 2007 Dataset” and “DARPA 2000 Dataset” and the efficiency of the proposed system has been tested based on the following performance parameters: Accuracy, Detection Rate and False Positive Rate and the result obtained from the proposed system has been found that it has enhanced accuracy and detection rate with low false positive rate.
