Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In...Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.展开更多
Passive optical networks (PONs) offer suffi-cient bandwidth to transfer huge amount having different packet sizes and data rates being generated by fusion of various networks. Additionally, multiple optical line termi...Passive optical networks (PONs) offer suffi-cient bandwidth to transfer huge amount having different packet sizes and data rates being generated by fusion of various networks. Additionally, multiple optical line terminals (OLTs) PONs reduce the computational com-plexity of data processing for nonuniform traffic. However, in order to improve the bandwidth allocation efficiency of a mixture of service providers, dynamic bandwidth algo-rithm (DBA) is needed for uplink communication. In this paper, a PON based open access network (OAN) is analyzed for bi-directional communication at various data rates. Multiple wavelengths are used to modulate the data of various service providers to evade the complicated DBA for uplink data broadcasting. The performance of the network is reported in terms of bandwidth exploitation, uplink effectiveness, overhead-to-data ratio and time cycle duration. The network is analyzed at various data rates to reveal the data accommodation capacity.展开更多
文摘Due to our increased dependence on Internet and growing number of intrusion incidents, building effective intrusion detection systems are essential for protecting Internet resources and yet it is a great challenge. In literature, many researchers utilized Artificial Neural Networks (ANN) in supervised learning based intrusion detection successfully. Here, ANN maps the network traffic into predefined classes i.e. normal or specific attack type based upon training from label dataset. However, for ANN-based IDS, detection rate (DR) and false positive rate (FPR) are still needed to be improved. In this study, we propose an ensemble approach, called MANNE, for ANN-based IDS that evolves ANNs by Multi Objective Genetic algorithm to solve the problem. It helps IDS to achieve high DR, less FPR and in turn high intrusion detection capability. The procedure of MANNE is as follows: firstly, a Pareto front consisting of a set of non-dominated ANN solutions is created using MOGA, which formulates the base classifiers. Subsequently, based upon this pool of non-dominated ANN solutions as base classifiers, another Pareto front consisting of a set of non-dominated ensembles is created which exhibits classification tradeoffs. Finally, prediction aggregation is done to get final ensemble prediction from predictions of base classifiers. Experimental results on the KDD CUP 1999 dataset show that our proposed ensemble approach, MANNE, outperforms ANN trained by Back Propagation and its ensembles using bagging & boosting methods in terms of defined performance metrics. We also compared our approach with other well-known methods such as decision tree and its ensembles using bagging & boosting methods.
文摘Passive optical networks (PONs) offer suffi-cient bandwidth to transfer huge amount having different packet sizes and data rates being generated by fusion of various networks. Additionally, multiple optical line terminals (OLTs) PONs reduce the computational com-plexity of data processing for nonuniform traffic. However, in order to improve the bandwidth allocation efficiency of a mixture of service providers, dynamic bandwidth algo-rithm (DBA) is needed for uplink communication. In this paper, a PON based open access network (OAN) is analyzed for bi-directional communication at various data rates. Multiple wavelengths are used to modulate the data of various service providers to evade the complicated DBA for uplink data broadcasting. The performance of the network is reported in terms of bandwidth exploitation, uplink effectiveness, overhead-to-data ratio and time cycle duration. The network is analyzed at various data rates to reveal the data accommodation capacity.
