Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this pap...Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.展开更多
In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By le...In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By leveraging the adaptive capabilities of the ENN framework,the antenna design efficiency is significantly improved,enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications.Extensive characterization confirms that the proposed MCLPA achieves outstanding performance,including an ultra-broad operational bandwidth of 372 GHz(0.135-0.507 THz),a peak gain of 5.51 dBi,an optimal S-parameter(S11)of−13.68 dB,and a maximum radiation efficiency of 82.39%.In addition,the MCLPA exhibits superior sensitivity,low noise susceptibility,and fast response,which are key attributes for reliable and precise THz detection.When configured in array form,the design further enhances gain and directional responsiveness,demonstrating the scalability and deployment potential of the MCLPA.This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering,introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation.By substantially reducing design time and cost while achieving exceptional performance,the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems,offering broad implications for future high-frequency technologies.展开更多
基金Supported in part by the National Natural Science Foundation of China (No.60272046, No.60102011), Na-tional High Technology Project of China (No.2002AA143010), Natural Science Foundation of Jiangsu Province (No.BK2001042), and the Foundation for Excellent Doctoral Dissertation of Southeast Univer-sity (No.YBJJ0412).
文摘Several data mining techniques such as Hidden Markov Model (HMM), artificial neural network, statistical techniques and expert systems are used to model network packets in the field of intrusion detection. In this paper a novel intrusion detection mode based on understandable Neural Network Tree (NNTree) is pre-sented. NNTree is a modular neural network with the overall structure being a Decision Tree (DT), and each non-terminal node being an Expert Neural Network (ENN). One crucial advantage of using NNTrees is that they keep the non-symbolic model ENN’s capability of learning in changing environments. Another potential advantage of using NNTrees is that they are actually “gray boxes” as they can be interpreted easily if the num-ber of inputs for each ENN is limited. We showed through experiments that the trained NNTree achieved a simple ENN at each non-terminal node as well as a satisfying recognition rate of the network packets dataset. We also compared the performance with that of a three-layer backpropagation neural network. Experimental results indicated that the NNTree based intrusion detection model achieved better performance than the neural network based intrusion detection model.
基金support from the Natural Sciences and Engineering Research Council of Canada(NSERC)and the Micro-Nano Technology(MNT)program facilitated by CMC Microsystems.
文摘In this work,we introduce a novel Micro Circular Log-Periodic Antenna(MCLPA)optimized with an advanced Evolutionary Neural Network(ENN)algorithm,specifically designed to enhance terahertz(THz)radiation detection.By leveraging the adaptive capabilities of the ENN framework,the antenna design efficiency is significantly improved,enabling rapid prototyping and yielding highly optimized structures tailored for practical THz applications.Extensive characterization confirms that the proposed MCLPA achieves outstanding performance,including an ultra-broad operational bandwidth of 372 GHz(0.135-0.507 THz),a peak gain of 5.51 dBi,an optimal S-parameter(S11)of−13.68 dB,and a maximum radiation efficiency of 82.39%.In addition,the MCLPA exhibits superior sensitivity,low noise susceptibility,and fast response,which are key attributes for reliable and precise THz detection.When configured in array form,the design further enhances gain and directional responsiveness,demonstrating the scalability and deployment potential of the MCLPA.This ENN-driven MCLPA represents a significant breakthrough in THz antenna engineering,introducing a transformative design paradigm that synergistically integrates algorithmic intelligence with structural innovation.By substantially reducing design time and cost while achieving exceptional performance,the proposed ENN framework sets a new benchmark for the development of next-generation THz detection and communication systems,offering broad implications for future high-frequency technologies.
