Most unsupervised or semisupervised hyperspectral anomaly detection(HAD)methods train background reconstruction models in the original spectral domain.However,due to the noise and spatial resolution limitations,there ...Most unsupervised or semisupervised hyperspectral anomaly detection(HAD)methods train background reconstruction models in the original spectral domain.However,due to the noise and spatial resolution limitations,there may be a lack of discrimination between backgrounds and anomalies.This makes it easy for the autoencoder to capture the lowlevel features shared between the two,thereby increasing the difficulty of separating anomalies from the backgrounds,which runs counter to the purpose of HAD.To this end,the authors map the original spectrums to the fractional Fourier domain(FrFD)and reformulate it as a mapping task in which restoration errors are employed to distinguish background and anomaly.This study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for HAD.Specifically,the depth separable features of backgrounds and anomalies are enhanced in the FrFD.Due to the semisupervised approach,FTSGAN needs to learn the embedded features of the backgrounds,thus mapping and restoring them from the FrFD to the original spectral domain.This strategy effectively prevents the model from focussing on the numerical equivalence of input and output,and restricts the ability of FTSGAN to restore anomalies.The comparison and analysis of the experiments verify that the proposed method is competitive.展开更多
The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identific...The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.展开更多
Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongl...Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.展开更多
The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels.With the massive number of connected devices,opportunities for poten...The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels.With the massive number of connected devices,opportunities for potential network attacks are nearly unlimited.An additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)attacks.In this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems.The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies.An additional feature of the proposed IDS is that it is trained with an optimized dataset,where the number of features is reduced by 94%without classification accuracy loss.Thus,the proposed IDS remains stable in response to slight system perturbations,which do not represent network anomalies.The proposed approach is evaluated under different simulation scenarios and provides a 99%detection accuracy over known datasets while reducing the training time by an order of magnitude.展开更多
Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given scene.As it is an ill-posed problem,to fit the projection function from the given scene to the 3D stru...Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given scene.As it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated data.Such pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or water.The widespread application of deep learning further intensifies the demand for large amounts of annotated data.Therefore,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of data.In this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given scene.First,semantic information is employed to make the depth inference more accurate.Second,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic segmentation.We compare our model with state-of-the-art methods.The experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.展开更多
Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision...Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification.However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.展开更多
基金supported by the National Natural Science Foundation of China under Grant 62161160336Grant 41871245in part by the Belgium Vlaio project(AI ICON‐2021‐0599:Smart industrial spectral cameras via artificial intelligence).
文摘Most unsupervised or semisupervised hyperspectral anomaly detection(HAD)methods train background reconstruction models in the original spectral domain.However,due to the noise and spatial resolution limitations,there may be a lack of discrimination between backgrounds and anomalies.This makes it easy for the autoencoder to capture the lowlevel features shared between the two,thereby increasing the difficulty of separating anomalies from the backgrounds,which runs counter to the purpose of HAD.To this end,the authors map the original spectrums to the fractional Fourier domain(FrFD)and reformulate it as a mapping task in which restoration errors are employed to distinguish background and anomaly.This study proposes a novel frequency‐to‐spectrum mapping generative adversarial network for HAD.Specifically,the depth separable features of backgrounds and anomalies are enhanced in the FrFD.Due to the semisupervised approach,FTSGAN needs to learn the embedded features of the backgrounds,thus mapping and restoring them from the FrFD to the original spectral domain.This strategy effectively prevents the model from focussing on the numerical equivalence of input and output,and restricts the ability of FTSGAN to restore anomalies.The comparison and analysis of the experiments verify that the proposed method is competitive.
基金This work is supported by the Science and Technology Project of State Grid Jiangsu Electric Power Co.,Ltd.under Grant No.J2020068.
文摘The rapidly increasing popularity of mobile devices has changed the methods with which people access various network services and increased net-work traffic markedly.Over the past few decades,network traffic identification has been a research hotspot in the field of network management and security mon-itoring.However,as more network services use encryption technology,network traffic identification faces many challenges.Although classic machine learning methods can solve many problems that cannot be solved by port-and payload-based methods,manually extract features that are frequently updated is time-consuming and labor-intensive.Deep learning has good automatic feature learning capabilities and is an ideal method for network traffic identification,particularly encrypted traffic identification;Existing recognition methods based on deep learning primarily use supervised learning methods and rely on many labeled samples.However,in real scenarios,labeled samples are often difficult to obtain.This paper adjusts the structure of the auxiliary classification generation adversarial network(ACGAN)so that it can use unlabeled samples for training,and use the wasserstein distance instead of the original cross entropy as the loss function to achieve semisupervised learning.Experimental results show that the identification accuracy of ISCX and USTC data sets using the proposed method yields markedly better performance when the number of labeled samples is small compared to that of convolutional neural network(CNN)based classifier.
基金The National Natural Science Foundation of China under contract No.61971455.
文摘Ship detection using synthetic aperture radar(SAR)plays an important role in marine applications.The existing methods are capable of quickly obtaining many candidate targets,but numerous non-ship objects may be wrongly detected in complex backgrounds.These non-ship false alarms can be excluded by training discriminators,and the desired accuracy is obtained with enough verified samples.However,the reliable verification of targets in large-scene SAR images still inevitably requires manual interpretation,which is difficult and time consuming.To address this issue,a semisupervised heterogeneous ensemble ship target discrimination method based on a tri-training scheme is proposed to take advantage of the plentiful candidate targets.Specifically,various features commonly used in SAR image target discrimination are extracted,and several acknowledged classification models and their classic variants are investigated.Multiple discriminators are constructed by dividing these features into different groups and pairing them with each model.Then,the performance of all the discriminators is tested,and better discriminators are selected for implementing the semisupervised training process.These strategies enhance the diversity and reliability of the discriminators,and their heterogeneous ensemble makes more correct judgments on candidate targets,which facilitates further positive training.Experimental results demonstrate that the proposed method outperforms traditional tritraining.
基金This work was supported by the Slovak Research and Development Agency,project number APVV-18-0214by the Scientific Grant Agency of the Ministry of Education,science,research and sport of the Slovak Republic under the contract:1/0268/19by the Ukrainian government projects No.0120U102201“Development the methods and unified software-hardware means for the deployment of the energy efficient intent-based multi-purpose information and communication networks,”and No.0120U100674,“Designing the novel decentralized mobile network based on blockchain architecture and artificial intelligence for 5G/6G development in Ukraine.”。
文摘The extensive proliferation of modern information services and ubiquitous digitization of society have raised cybersecurity challenges to new levels.With the massive number of connected devices,opportunities for potential network attacks are nearly unlimited.An additional problem is that many low-cost devices are not equippedwith effective security protection so that they are easily hacked and applied within a network of bots(botnet)to perform distributed denial of service(DDoS)attacks.In this paper,we propose a novel intrusion detection system(IDS)based on deep learning that aims to identify suspicious behavior in modern heterogeneous information systems.The proposed approach is based on a deep recurrent autoencoder that learns time series of normal network behavior and detects notable network anomalies.An additional feature of the proposed IDS is that it is trained with an optimized dataset,where the number of features is reduced by 94%without classification accuracy loss.Thus,the proposed IDS remains stable in response to slight system perturbations,which do not represent network anomalies.The proposed approach is evaluated under different simulation scenarios and provides a 99%detection accuracy over known datasets while reducing the training time by an order of magnitude.
基金supported in part by the National High Technology Research and Development Program of China(Grant No.2021YFF0900500)the National Natural Science Foundation of China(Grant Nos.61972115 and 61872116)。
文摘Depth estimation is a fundamental computer vision problem that infers three-dimensional(3D)structures from a given scene.As it is an ill-posed problem,to fit the projection function from the given scene to the 3D structure,traditional methods generally require mass amounts of annotated data.Such pixel-level annotation is quite labor consuming,especially when addressing reflective surfaces such as mirrors or water.The widespread application of deep learning further intensifies the demand for large amounts of annotated data.Therefore,it is urgent and necessary to propose a framework that is able to reduce the requirement on the amount of data.In this paper,we propose a novel semisupervised learning framework to infer the 3D structure from the given scene.First,semantic information is employed to make the depth inference more accurate.Second,we make both the depth estimation and semantic segmentation coarse-to-fine frameworks;thus,the depth estimation can be gradually guided by semantic segmentation.We compare our model with state-of-the-art methods.The experimental results demonstrate that our method is better than many supervised learning-based methods,which proves the effectiveness of the proposed method.
基金supported by the National Key R&D Program of China (2018YFB2101300)。
文摘Radiation source identification plays an important role in non-cooperative communication scene and numerous methods have been proposed in this field. Deep learning has gained popularity in a variety of computer vision tasks. Recently, it has also been successfully applied for radiation source identification.However, training deep neural networks for classification requires a large number of labeled samples, and in non-cooperative applications, it is unrealistic. This paper proposes a method for the unlabeled samples of unknown radiation source. It uses semi-supervised learning to detect unlabeled samples and label new samples automatically. It avoids retraining the neural network with parameter-transfer learning. The results show that compared with the traditional algorithms, the proposed algorithm can offer better accuracy.
