Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Fac...Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level,an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed.This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms,including the emergence of swarm intelligence,information network construction and collaborative mechanism design.Then,based on the framework,the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects:topology dynamics and strategy dynamics.Next,the unmanned swarms’community network is designed,and the network characteristics are analyzed.Moreover,the mechanism characteristics are analyzed by numerical simulation,focusing on the impact of key parameters,such as cost,benefit coefficient and adjustment rate on the level of swarm cooperation.Finally,the conclusion is made,which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.展开更多
In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extract...In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.展开更多
To analyze the behavioral model of the command,control,communication,computer,intelligence,surveillance,reconnaissance(C4ISR)architecture,we propose an executable modeling and analyzing approach to it.First,the meta c...To analyze the behavioral model of the command,control,communication,computer,intelligence,surveillance,reconnaissance(C4ISR)architecture,we propose an executable modeling and analyzing approach to it.First,the meta concept model of the C4ISR architecture is introduced.According to the meta concept model,we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML.Then,we define the concrete syntax and executable activity algebra(EAA)semantics for executable models.The semantics functions are introduced to translating the syntax description of executable models into the item of EAA.To support the execution of models,we propose the executable rules which are the structural operational semantics of EAA.Finally,an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.展开更多
How to correctly acquire the appropriate features is a primary problem in network protocol recognition field.Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recogniti...How to correctly acquire the appropriate features is a primary problem in network protocol recognition field.Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy,a network protocol recognition method based on Convolutional Neural Network(CNN)is proposed.The method utilizes deep learning technique,and it processes network flows automatically.Firstly,normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN.Then,an improved classification model named Ptr CNN is built,which can automatically extract the appropriate features of network protocols.Finally,the classification model is trained to recognize the network protocols.The proposed approach is compared with several machine learning methods.Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.展开更多
The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some w...The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.展开更多
As the scale of current systems become larger and larger and their complexity is increasing gradually,research on executable models in the design phase becomes significantly important as it is helpful to simulate the ...As the scale of current systems become larger and larger and their complexity is increasing gradually,research on executable models in the design phase becomes significantly important as it is helpful to simulate the execution process and capture defects of a system in advance.Meanwhile,the capability of a system becomes so important that stakeholders tend to emphasize their capability requirements when developing a system.To deal with the lack of official specifications and the fundamental theory basis for capability requirement,we propose a cooperative capability requirements(CCR)meta-model as a theory basis for researchers to refer to in this research domain,in which we provide detailed definition of the CCR concepts,associations and rules.Moreover,we also propose an executable framework,which may enable modelers to simulate the execution process of a system in advance and do well in filling the inconsistency and semantic gaps between stakeholders’requirements and their models.The primary working mechanism of the framework is to transform the Alf activity meta-model into the communicating sequential process(CSP)process meta-model based on some mapping rules,after which the internal communication mechanism between process nodes is designed to smooth the execution of behaviors in a CSP system.Moreover,a validation method is utilized to check the correctness and consistency of the models,and a self-fixing mechanism is used to fix the errors and warnings captured during the validation process automatically.Finally,a validation report is generated and fed back to the modelers for system optimization.展开更多
To analyze and optimize the weapon system of systems(WSOS)scheduling process,a new method based on robust capabilities for WSOS scheduling optimization is proposed.First,we present an activity network to represent the...To analyze and optimize the weapon system of systems(WSOS)scheduling process,a new method based on robust capabilities for WSOS scheduling optimization is proposed.First,we present an activity network to represent the military mission.The member systems need to be reasonably assigned to perform different activities in the mission.Then we express the problem as a set partitioning formulation with novel columns(activity flows).A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem(WSOSSP).The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements.Finally,we discuss this method in several test cases.The results show that the solution can reduce the makespan of the mission remarkably.展开更多
基金supported by the National Natural Science Foundation of China(71901217)the Key Primary Research Project of Primary Strengthening Program(2020-JCJQ-ZD-007).
文摘Autonomous cooperation of unmanned swarms is the research focus on“new combat forces”and“disruptive technologies”in military fields.The mechanism design is the fundamental way to realize autonomous cooperation.Facing the realistic requirements of a swarm network dynamic adjustment under the background of high dynamics and strong confrontation and aiming at the optimization of the coordination level,an adaptive dynamic reconfiguration mechanism of unmanned swarm topology based on an evolutionary game is designed.This paper analyzes military requirements and proposes the basic framework of autonomous cooperation of unmanned swarms,including the emergence of swarm intelligence,information network construction and collaborative mechanism design.Then,based on the framework,the adaptive dynamic reconfiguration mechanism is discussed in detail from two aspects:topology dynamics and strategy dynamics.Next,the unmanned swarms’community network is designed,and the network characteristics are analyzed.Moreover,the mechanism characteristics are analyzed by numerical simulation,focusing on the impact of key parameters,such as cost,benefit coefficient and adjustment rate on the level of swarm cooperation.Finally,the conclusion is made,which is expected to provide a theoretical reference and decision support for cooperative mode design and combat effectiveness generation of unmanned swarm operations.
基金This work is supported by the National Key R&D Program of China(2017YFB0802900).
文摘In recent years,many unknown protocols are constantly emerging,and they bring severe challenges to network security and network management.Existing unknown protocol recognition methods suffer from weak feature extraction ability,and they cannot mine the discriminating features of the protocol data thoroughly.To address the issue,we propose an unknown application layer protocol recognition method based on deep clustering.Deep clustering which consists of the deep neural network and the clustering algorithm can automatically extract the features of the input and cluster the data based on the extracted features.Compared with the traditional clustering methods,deep clustering boasts of higher clustering accuracy.The proposed method utilizes network-in-network(NIN),channel attention,spatial attention and Bidirectional Long Short-term memory(BLSTM)to construct an autoencoder to extract the spatial-temporal features of the protocol data,and utilizes the unsupervised clustering algorithm to recognize the unknown protocols based on the features.The method firstly extracts the application layer protocol data from the network traffic and transforms the data into one-dimensional matrix.Secondly,the autoencoder is pretrained,and the protocol data is compressed into low dimensional latent space by the autoencoder and the initial clustering is performed with K-Means.Finally,the clustering loss is calculated and the classification model is optimized according to the clustering loss.The classification results can be obtained when the classification model is optimal.Compared with the existing unknown protocol recognition methods,the proposed method utilizes deep clustering to cluster the unknown protocols,and it can mine the key features of the protocol data and recognize the unknown protocols accurately.Experimental results show that the proposed method can effectively recognize the unknown protocols,and its performance is better than other methods.
文摘To analyze the behavioral model of the command,control,communication,computer,intelligence,surveillance,reconnaissance(C4ISR)architecture,we propose an executable modeling and analyzing approach to it.First,the meta concept model of the C4ISR architecture is introduced.According to the meta concept model,we construct the executable meta models of the C4ISR architecture by extending the meta models of fUML.Then,we define the concrete syntax and executable activity algebra(EAA)semantics for executable models.The semantics functions are introduced to translating the syntax description of executable models into the item of EAA.To support the execution of models,we propose the executable rules which are the structural operational semantics of EAA.Finally,an area air defense of the C4ISR system is used to illustrate the feasibility of the approach.
基金supported by the National Key R&D Program of China(2017YFB0802900).
文摘How to correctly acquire the appropriate features is a primary problem in network protocol recognition field.Aiming to avoid the trouble of artificially extracting features in traditional methods and improve recognition accuracy,a network protocol recognition method based on Convolutional Neural Network(CNN)is proposed.The method utilizes deep learning technique,and it processes network flows automatically.Firstly,normalization is performed on the intercepted network flows and they are mapped into two-dimensional matrix which will be used as the input of CNN.Then,an improved classification model named Ptr CNN is built,which can automatically extract the appropriate features of network protocols.Finally,the classification model is trained to recognize the network protocols.The proposed approach is compared with several machine learning methods.Experimental results show that the tailored CNN can not only improve protocol recognition accuracy but also ensure the fast convergence of classification model and reduce the classification time.
基金National Natural Science Foundation of China(grant number 61801512,grant number 62071484)Natural Science Foundation of Jiangsu Province(grant number BK20180080)to provide fund for conducting experiments。
文摘The object detectors can precisely detect the camouflaged object beyond human perception.The investigations reveal that the CNNs-based(Convolution Neural Networks)detectors are vulnerable to adversarial attacks.Some works can fool detectors by crafting the adversarial camouflage attached to the object,leading to wrong prediction.It is hard for military operations to utilize the existing adversarial camouflage due to its conspicuous appearance.Motivated by this,this paper proposes the Dual Attribute Adversarial Camouflage(DAAC)for evading the detection by both detectors and humans.Generating DAAC includes two steps:(1)Extracting features from a specific type of scene to generate individual soldier digital camouflage;(2)Attaching the adversarial patch with scene features constraint to the individual soldier digital camouflage to generate the adversarial attribute of DAAC.The visual effects of the individual soldier digital camouflage and the adversarial patch will be improved after integrating with the scene features.Experiment results show that objects camouflaged by DAAC are well integrated with background and achieve visual concealment while remaining effective in fooling object detectors,thus evading the detections by both detectors and humans in the digital domain.This work can serve as the reference for crafting the adversarial camouflage in the physical world.
基金supported by the National Key R&D Program of China(2018YFC0806900)the China Postdoctoral Science Foundation Funded Project(2018M633757)+1 种基金the Primary Research&Development Plan of Jiangsu Province(BE2016904,BE2017616,BE2018754,BE2019762)Jiangsu Province Postdoctoral Science Foundation Funded Project(2019K185).
文摘As the scale of current systems become larger and larger and their complexity is increasing gradually,research on executable models in the design phase becomes significantly important as it is helpful to simulate the execution process and capture defects of a system in advance.Meanwhile,the capability of a system becomes so important that stakeholders tend to emphasize their capability requirements when developing a system.To deal with the lack of official specifications and the fundamental theory basis for capability requirement,we propose a cooperative capability requirements(CCR)meta-model as a theory basis for researchers to refer to in this research domain,in which we provide detailed definition of the CCR concepts,associations and rules.Moreover,we also propose an executable framework,which may enable modelers to simulate the execution process of a system in advance and do well in filling the inconsistency and semantic gaps between stakeholders’requirements and their models.The primary working mechanism of the framework is to transform the Alf activity meta-model into the communicating sequential process(CSP)process meta-model based on some mapping rules,after which the internal communication mechanism between process nodes is designed to smooth the execution of behaviors in a CSP system.Moreover,a validation method is utilized to check the correctness and consistency of the models,and a self-fixing mechanism is used to fix the errors and warnings captured during the validation process automatically.Finally,a validation report is generated and fed back to the modelers for system optimization.
基金supported by the National Key R&D Program of China(2018YFC0806900)the China Postdoctoral Science Foundation Funded Project(2018M633757)+1 种基金the Primary Research&Development Plan of Jiangsu Province(BE2017616,BE20187540,BE2019762,BE2020729)the Jiangsu Province Postdoctoral Science Foundation Funded Project(2019K185).
文摘To analyze and optimize the weapon system of systems(WSOS)scheduling process,a new method based on robust capabilities for WSOS scheduling optimization is proposed.First,we present an activity network to represent the military mission.The member systems need to be reasonably assigned to perform different activities in the mission.Then we express the problem as a set partitioning formulation with novel columns(activity flows).A heuristic branch-and-price algorithm is designed based on the model of the WSOS scheduling problem(WSOSSP).The algorithm uses the shortest resource-constrained path planning to generate robust activity flows that meet the capability requirements.Finally,we discuss this method in several test cases.The results show that the solution can reduce the makespan of the mission remarkably.
