An MI.P(Multi-Layer Perception)/Elman neural network is proposed in thispaper, which realizes classification with memory of past events using the real-time classificationof MI.P and the memorial functionality of Elman...An MI.P(Multi-Layer Perception)/Elman neural network is proposed in thispaper, which realizes classification with memory of past events using the real-time classificationof MI.P and the memorial functionality of Elman. The system's sensitivity for the memory of pastevents ean be easily reconfigured without retraining the whole network. This approach can he usedfor both misuse and anomaly detection system. The intrusion detection systems(TDSs) using the hybridMLP/Elman neural network are evaluated by the intrusion detection evaluation data sponsored by U.S.Defense Advanced Research Projects Agency CDARPA) Ihc results of experiment are presented inReceiver Operating Characteristic CROC) curves. Thc capabilites of these IDSs to identify DenyofService(DOS) and probing attacks are enhanced.展开更多
针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirec...针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。展开更多
针对低轨道卫星信道质量变化迅速、信道参数“过时”的问题,提出了一种基于注意力机制的卷积神经和双向长短时记忆神经网络(attention-convolutional neural network and bi-directional long-short term memory neural network,AT-CNN-...针对低轨道卫星信道质量变化迅速、信道参数“过时”的问题,提出了一种基于注意力机制的卷积神经和双向长短时记忆神经网络(attention-convolutional neural network and bi-directional long-short term memory neural network,AT-CNN-BiLSTM)融合的信道预测方法。该方法由信号预处理、网络训练和信号预测3部分组成。首先在高斯白噪声条件下模拟室外卫星信号,得到卫星信号的训练集和测试集;然后将训练集输入构建的训练网络进行特征提取;最后将测试数据输入网络进行预测分析。仿真结果表明,在与其他4种人工智能方法的对比中,所提出的混合神经网络能够在较快的收敛速度下达到较高的准确率(91.8%),有效地缓解了低轨道卫星信道参数“过时”的现状,对提升卫星通信质量和节省卫星信道资源有良好的改善作用。展开更多
Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,du...Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.展开更多
Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classif...Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.展开更多
文摘An MI.P(Multi-Layer Perception)/Elman neural network is proposed in thispaper, which realizes classification with memory of past events using the real-time classificationof MI.P and the memorial functionality of Elman. The system's sensitivity for the memory of pastevents ean be easily reconfigured without retraining the whole network. This approach can he usedfor both misuse and anomaly detection system. The intrusion detection systems(TDSs) using the hybridMLP/Elman neural network are evaluated by the intrusion detection evaluation data sponsored by U.S.Defense Advanced Research Projects Agency CDARPA) Ihc results of experiment are presented inReceiver Operating Characteristic CROC) curves. Thc capabilites of these IDSs to identify DenyofService(DOS) and probing attacks are enhanced.
文摘针对具有时频特性的雷达信号,传统的雷达信号识别方法已经无法满足对信号类型精准识别的需求,因此需要通过采集并分析雷达信号脉内的时频特征实现对目标雷达的具体信息进行有效评估。设计了一种卷积-双向长短时记忆(Convolution-Bidirectional Long Short-Term Memory,CNN-BiLSTM)混合神经网络模型,主要通过BiLSTM的时序记忆特性深度挖掘雷达信号的时域特征,结合权值共享特性和CNN层捕获雷达信号的时频特征,再利用二者信号特征联合完成对雷达信号调制方式的识别。通过对比实验验证,所提方法对若干种雷达信号的识别具有较高的准确度,平均值达到95.349%;优于只使用单一特征的网络和传统算法,具有良好的抗噪声能力。
文摘针对低轨道卫星信道质量变化迅速、信道参数“过时”的问题,提出了一种基于注意力机制的卷积神经和双向长短时记忆神经网络(attention-convolutional neural network and bi-directional long-short term memory neural network,AT-CNN-BiLSTM)融合的信道预测方法。该方法由信号预处理、网络训练和信号预测3部分组成。首先在高斯白噪声条件下模拟室外卫星信号,得到卫星信号的训练集和测试集;然后将训练集输入构建的训练网络进行特征提取;最后将测试数据输入网络进行预测分析。仿真结果表明,在与其他4种人工智能方法的对比中,所提出的混合神经网络能够在较快的收敛速度下达到较高的准确率(91.8%),有效地缓解了低轨道卫星信道参数“过时”的现状,对提升卫星通信质量和节省卫星信道资源有良好的改善作用。
基金Aeronautical Science Foundation of China(No.2018ZC51022)。
文摘Convolutional Neural Networks(CNNs)have recently attracted much attention in the ship detection from Synthetic Aperture Radar(SAR)images.However,compared with optical images,SAR ones are hard to understand.Moreover,due to the high similarity between the man-made targets near shore and inshore ships,the classical methods are unable to achieve effective detection of inshore ships.To mitigate the influence of onshore ship-like objects,this paper proposes an inshore ship detection method in SAR images by using hybrid features.Firstly,the sea-land segmentation is applied in the pre-processing to exclude obvious land regions from SAR images.Then,a CNN model is designed to extract deep features for identifying potential ship targets in both inshore and offshore water.On this basis,the high-energy point number of amplitude spectrum is further introduced as an important and delicate feature to suppress false alarms left.Finally,to verify the effectiveness of the proposed method,numerical and comparative studies are carried out in experiments on Sentinel-1 SAR images.
文摘Machine learning techniques such as artificial neural networks are seeing increased use in the examination of communication network research questions.Central to many of these research questions is the need to classify packets and improve visibility.Multi-Layer Perceptron(MLP)neural networks and Convolutional Neural Networks(CNNs)have been used to successfully identify individual packets.However,some datasets create instability in neural network models.Machine learning can also be subject to data injection and misclassification problems.In addition,when attempting to address complex communication network challenges,extremely high classification accuracy is required.Neural network ensembles can work towards minimizing or even eliminating some of these problems by comparing results from multiple models.After ensembles tuning,training time can be reduced,and a viable and effective architecture can be obtained.Because of their effectiveness,ensembles can be utilized to defend against data poisoning attacks attempting to create classification errors.In this work,ensemble tuning and several voting strategies are explored that consistently result in classification accuracy above 99%.In addition,ensembles are shown to be effective against these types of attack by maintaining accuracy above 98%.
