Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive...Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.展开更多
The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is ...The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.展开更多
Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed un...Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.展开更多
Premature ventricular contractions(PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram(ECG), which is onerous a...Premature ventricular contractions(PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram(ECG), which is onerous and time-consuming. In this study, deep learning was applied to develop models that can detect PVCs in children automatically. This computer-aided diagnosis model achieved high accuracy while sustained stable performance. It could save time and repeated efforts for physicians, enabling them to focus on more complicated tasks.This study is a first step toward children's PVC auto-detection in clinics. Further study will improve the model's performance with optimized structure and more data in different sources, while facing the challenges of the variety and uncertainty of children's ECG with heart diseases.展开更多
Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mos...Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.展开更多
In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficien...In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.展开更多
The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conve...The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conventional smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and accurate.Although deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within contracts.Therefore,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning techniques.Firstly,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart contracts.Then,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated units.Ultimately,the revised node features and edge features are concatenated through amulti-head attentionmechanism.The result of the splicing is a global representation of the entire feature graph.Our method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.展开更多
In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building infrastructure.All the developed and developing countries focus on bu...In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building infrastructure.All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation.At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries.As part of the innovations happening in the industrial sector,a new concept termed as‘smart manufacturing’has emerged,which employs the benefits of emerging technologies like internet of things and cloud computing.Cloud services deliver an on-demand access to computing,storage,and infrastructural platforms for the industrial users through Internet.In the recent era of information technology the number of business and individual users of cloud services have been increased and larger volumes of data is being processed and stored in it.As a consequence,the data breaches in the cloud services are also increasing day by day.Due to various security vulnerabilities in the cloud architecture;as a result the cloud environment has become non-resilient.To restore the normal behavior of the cloud,detect the deviations,and achieve higher resilience,anomaly detection becomes essential.The deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal events.This paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more resilient.The deep learning models are trained using features extracted from the system level and network level performance metrics observed in the Transfer Control Protocol(TCP)traces of the simulation.The experimental results of the proposed approach demonstrate a superior performance in terms of higher detection rate and lower false alarm rate when compared to the Support Vector Machine(SVM).展开更多
Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is co...Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.展开更多
In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model ...In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present.In this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent unitmodel.The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics.Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.展开更多
In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting...In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies.The presence of anomalies is also one of the disadvantages,internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on,this a case of true benign detected as false.Deep learning is very adept at dealing with such cases,but sometimes it has its own faults when dealing benign cases.Here we tend to adopt a dynamic control system(DCSYS)that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies.Its performance is compared with artificial neural network auto-encoders to define its predictive power.Results show that though physical systems can adapt securely,it can be used for network data packets to identify true benign cases.展开更多
A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encry...A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encryption efficiency and improving the hiding quality.First,AiEhS employs an auto-encoder to compress the SPI and designs a new encryption method for encrypting the compressed image to obtain a cipher image,reaching the first layer of encryption.Second,pixels in the cipher image are decomposed,combined,and scrambled to obtain another scrambled image.Thereafter,a trained RNN model is used to embed this scrambled image into a selected carrier image,resulting in a new carrier image hiding secrets,thus realizing the second layer of hiding.Moreover,AiEhS produces a pseudorandom sequence using a hyperchaotic map and constructs a new key model to achieve a plaintext dependency.The keys are then designed and distributed by the Rivest-Shamir-Adleman algorithm,effectively improving the security.Compared with traditional compressive-sensing-based image-hiding methods,the contributions of AiEhS are as follows:(1)A new scheme is designed using an auto-encoder to compress the SPI,which can reduce the time cost of both compression and reconstruction,thus accelerating the execution efficiency.(2)The scrambled image is hidden in a carrier image by RNN,which can increase the embedding amount and achieve better hiding quality.Furthermore,experiments show that AiEhS using deep learning can ensure better security and efficiency for image encryption and hiding,in contrast with the traditional image compression and embedding technique.For example,the peak signal-to-noise ratio for the reconstructed image exceeds 34 d B.展开更多
文摘Since the effectiveness of extracting fault features is not high under traditional bearing fault diagnosis method, a bearing fault diagnosis method based on Deep Auto-encoder Network (DAEN) optimized by Cloud Adaptive Particle Swarm Optimization (CAPSO) was proposed. On the basis of analyzing CAPSO and DAEN, the CAPSO-DAEN fault diagnosis model is built. The model uses the randomness and stability of CAPSO algorithm to optimize the connection weight of DAEN, to reduce the constraints on the weights and extract fault features adaptively. Finally, efficient and accurate fault diagnosis can be implemented with the Softmax classifier. The results of test show that the proposed method has higher diagnostic accuracy and more stable diagnosis results than those based on the DAEN, Support Vector Machine (SVM) and the Back Propagation algorithm (BP) under appropriate parameters.
文摘The accurate identification and diagnosis of chemical process faults are crucial for ensuring the safe and stable operation of production plants.The current hot topic in industrial process fault diagnosis research is data-driven methods.Most of the existing fault diagnosis methods focus on a single shallow or deep learning model.This paper proposes a novel hybrid fault diagnosis method to fully utilize various features to improve the accuracy of fault diagnosis.Furthermore,the method addresses the issue of incomplete data,which has been largely overlooked in the majority of existing research.Firstly,the variable data is effectively fitted using orthogonal non-negative matrix tri-factorization,and the missing data in the matrix is solved to construct a complete production condition relationship.Next,the support vector machine model and the deep residual contraction network model are trained in parallel to prediagnose process faults by mining linear and non-linear interaction features.Finally,a novel mapping relationship is established between the result and model levels using the multi-layer perceptron algorithm to complete the final diagnosis and evaluation of the fault.To demonstrate the effectiveness of the proposed method,we conducted extensive comparative experiments on the Tennessee Eastman dataset and the ethylene plant cracking unit dataset.The experimental results show that the method has advantages in different evaluation metrics.
基金supported in part by the National Natural Science Foundation of China(No.51606213)the National Major Science and Technology Projects(No.J2019-III-0010-0054)。
文摘Icing is an important factor threatening aircraft flight safety.According to the requirements of airworthiness regulations,aircraft icing safety assessment is needed to be carried out based on the ice shapes formed under different icing conditions.Due to the complexity of the icing process,the rapid assessment of ice shape remains an important challenge.In this paper,an efficient prediction model of aircraft icing is established based on the deep belief network(DBN)and the stacked auto-encoder(SAE),which are all deep neural networks.The detailed network structures are designed and then the networks are trained according to the samples obtained by the icing numerical computation.After that the model is applied on the ice shape evaluation of NACA0012 airfoil.The results show that the model can accurately capture the nonlinear behavior of aircraft icing and thus make an excellent ice shape prediction.The model provides an important tool for aircraft icing analysis.
文摘Premature ventricular contractions(PVCs) are abnormal heart beats that indicate potential heart diseases. Diagnosis of PVCs is made by physicians examining long recordings of electrocardiogram(ECG), which is onerous and time-consuming. In this study, deep learning was applied to develop models that can detect PVCs in children automatically. This computer-aided diagnosis model achieved high accuracy while sustained stable performance. It could save time and repeated efforts for physicians, enabling them to focus on more complicated tasks.This study is a first step toward children's PVC auto-detection in clinics. Further study will improve the model's performance with optimized structure and more data in different sources, while facing the challenges of the variety and uncertainty of children's ECG with heart diseases.
基金This work is supported in part by the National Science Foundation of China(Grant Nos.61672392,61373038)in part by the National Key Research and Development Program of China(Grant No.2016YFC1202204).
文摘Software defect prediction plays an important role in software quality assurance.However,the performance of the prediction model is susceptible to the irrelevant and redundant features.In addition,previous studies mostly regard software defect prediction as a single objective optimization problem,and multi-objective software defect prediction has not been thoroughly investigated.For the above two reasons,we propose the following solutions in this paper:(1)we leverage an advanced deep neural network-Stacked Contractive AutoEncoder(SCAE)to extract the robust deep semantic features from the original defect features,which has stronger discrimination capacity for different classes(defective or non-defective).(2)we propose a novel multi-objective defect prediction model named SMONGE that utilizes the Multi-Objective NSGAII algorithm to optimize the advanced neural network-Extreme learning machine(ELM)based on state-of-the-art Pareto optimal solutions according to the features extracted by SCAE.We mainly consider two objectives.One objective is to maximize the performance of ELM,which refers to the benefit of the SMONGE model.Another objective is to minimize the output weight norm of ELM,which is related to the cost of the SMONGE model.We compare the SCAE with six state-of-the-art feature extraction methods and compare the SMONGE model with multiple baseline models that contain four classic defect predictors and the MONGE model without SCAE across 20 open source software projects.The experimental results verify that the superiority of SCAE and SMONGE on seven evaluation metrics.
文摘In recent years,computer visionfinds wide applications in maritime surveillance with its sophisticated algorithms and advanced architecture.Auto-matic ship detection with computer vision techniques provide an efficient means to monitor as well as track ships in water bodies.Waterways being an important medium of transport require continuous monitoring for protection of national security.The remote sensing satellite images of ships in harbours and water bodies are the image data that aid the neural network models to localize ships and to facilitate early identification of possible threats at sea.This paper proposes a deep learning based model capable enough to classify between ships and no-ships as well as to localize ships in the original images using bounding box tech-nique.Furthermore,classified ships are again segmented with deep learning based auto-encoder model.The proposed model,in terms of classification,provides suc-cessful results generating 99.5%and 99.2%validation and training accuracy respectively.The auto-encoder model also produces 85.1%and 84.2%validation and training accuracies.Moreover the IoU metric of the segmented images is found to be of 0.77 value.The experimental results reveal that the model is accu-rate and can be implemented for automatic ship detection in water bodies consid-ering remote sensing satellite images as input to the computer vision system.
基金the Gansu Province Higher Education Institutions Industrial Support Program:Security Situational Awareness with Artificial Intelligence and Blockchain Technology.Project Number(2020C-29).
文摘The fast-paced development of blockchain technology is evident.Yet,the security concerns of smart contracts represent a significant challenge to the stability and dependability of the entire blockchain ecosystem.Conventional smart contract vulnerability detection primarily relies on static analysis tools,which are less efficient and accurate.Although deep learning methods have improved detection efficiency,they are unable to fully utilize the static relationships within contracts.Therefore,we have adopted the advantages of the above two methods,combining feature extraction mode of tools with deep learning techniques.Firstly,we have constructed corresponding feature extraction mode for different vulnerabilities,which are used to extract feature graphs from the source code of smart contracts.Then,the node features in feature graphs are fed into a graph convolutional neural network for training,and the edge features are processed using a method that combines attentionmechanismwith gated units.Ultimately,the revised node features and edge features are concatenated through amulti-head attentionmechanism.The result of the splicing is a global representation of the entire feature graph.Our method was tested on three types of data:Timestamp vulnerabilities,reentrancy vulnerabilities,and access control vulnerabilities,where the F1 score of our method reaches 84.63%,92.55%,and 61.36%.The results indicate that our method surpasses most others in detecting smart contract vulnerabilities.
文摘In the global scenario one of the important goals for sustainable development in industrial field is innovate new technology,and invest in building infrastructure.All the developed and developing countries focus on building resilient infrastructure and promote sustainable developments by fostering innovation.At this juncture the cloud computing has become an important information and communication technologies model influencing sustainable development of the industries in the developing countries.As part of the innovations happening in the industrial sector,a new concept termed as‘smart manufacturing’has emerged,which employs the benefits of emerging technologies like internet of things and cloud computing.Cloud services deliver an on-demand access to computing,storage,and infrastructural platforms for the industrial users through Internet.In the recent era of information technology the number of business and individual users of cloud services have been increased and larger volumes of data is being processed and stored in it.As a consequence,the data breaches in the cloud services are also increasing day by day.Due to various security vulnerabilities in the cloud architecture;as a result the cloud environment has become non-resilient.To restore the normal behavior of the cloud,detect the deviations,and achieve higher resilience,anomaly detection becomes essential.The deep learning architectures-based anomaly detection mechanisms uses various monitoring metrics characterize the normal behavior of cloud services and identify the abnormal events.This paper focuses on designing an intelligent deep learning based approach for detecting cloud anomalies in real time to make it more resilient.The deep learning models are trained using features extracted from the system level and network level performance metrics observed in the Transfer Control Protocol(TCP)traces of the simulation.The experimental results of the proposed approach demonstrate a superior performance in terms of higher detection rate and lower false alarm rate when compared to the Support Vector Machine(SVM).
文摘Occurrence of crimes has been on the constant rise despite the emerging discoveries and advancements in the technological field in the past decade.One of the most tedious tasks is to track a suspect once a crime is committed.As most of the crimes are committed by individuals who have a history of felonies,it is essential for a monitoring system that does not just detect the person’s face who has committed the crime,but also their identity.Hence,a smart criminal detection and identification system that makes use of the OpenCV Deep Neural Network(DNN)model which employs a Single Shot Multibox Detector for detection of face and an auto-encoder model in which the encoder part is used for matching the captured facial images with the criminals has been proposed.After detection and extraction of the face in the image by face cropping,the captured face is then compared with the images in the CriminalDatabase.The comparison is performed by calculating the similarity value between each pair of images that are obtained by using the Cosine Similarity metric.After plotting the values in a graph to find the threshold value,we conclude that the confidence rate of the encoder model is 0.75 and above.
基金supported by the National Natural Science Foundation of China(Grant Nos.62272120,62106030,U20B2046,62272119,61972105)the Technology Innovation and Application Development Projects of Chongqing(Grant Nos.cstc2021jscx-gksbX0032,cstc2021jscxgksbX0029).
文摘In recent years,with the great success of pre-trained language models,the pre-trained BERT model has been gradually applied to the field of source code understanding.However,the time cost of training a language model from zero is very high,and how to transfer the pre-trained language model to the field of smart contract vulnerability detection is a hot research direction at present.In this paper,we propose a hybrid model to detect common vulnerabilities in smart contracts based on a lightweight pre-trained languagemodel BERT and connected to a bidirectional gate recurrent unitmodel.The downstream neural network adopts the bidirectional gate recurrent unit neural network model with a hierarchical attention mechanism to mine more semantic features contained in the source code of smart contracts by using their characteristics.Our experiments show that our proposed hybrid neural network model SolBERT-BiGRU-Attention is fitted by a large number of data samples with smart contract vulnerabilities,and it is found that compared with the existing methods,the accuracy of our model can reach 93.85%,and the Micro-F1 Score is 94.02%.
文摘In network settings,one of the major disadvantages that threaten the network protocols is the insecurity.In most cases,unscrupulous people or bad actors can access information through unsecured connections by planting software or what we call malicious software otherwise anomalies.The presence of anomalies is also one of the disadvantages,internet users are constantly plagued by virus on their system and get activated when a harmless link is clicked on,this a case of true benign detected as false.Deep learning is very adept at dealing with such cases,but sometimes it has its own faults when dealing benign cases.Here we tend to adopt a dynamic control system(DCSYS)that addresses data packets based on benign scenario to truly report on false benign and exclude anomalies.Its performance is compared with artificial neural network auto-encoders to define its predictive power.Results show that though physical systems can adapt securely,it can be used for network data packets to identify true benign cases.
基金supported in part by the National Natural Science Foundation of China(Grant No.61972103)the Guangdong Basic and Applied Basics Research Foundation(Grant No.2023A1515011207)+1 种基金the Special Project in Key Area of General University in Guangdong Province of China(Grant No.2020ZDZX3064)the Innovation Team Project of General University in Guangdong Province of China(Grant No.2024KCXTD042)。
文摘A novel,asymmetric image encryption-hiding scheme(AiEhS)using a reversible neural network(RNN)was developed,in which deep learning is employed to compress and hide a secret plain image(SPI),thereby enhancing the encryption efficiency and improving the hiding quality.First,AiEhS employs an auto-encoder to compress the SPI and designs a new encryption method for encrypting the compressed image to obtain a cipher image,reaching the first layer of encryption.Second,pixels in the cipher image are decomposed,combined,and scrambled to obtain another scrambled image.Thereafter,a trained RNN model is used to embed this scrambled image into a selected carrier image,resulting in a new carrier image hiding secrets,thus realizing the second layer of hiding.Moreover,AiEhS produces a pseudorandom sequence using a hyperchaotic map and constructs a new key model to achieve a plaintext dependency.The keys are then designed and distributed by the Rivest-Shamir-Adleman algorithm,effectively improving the security.Compared with traditional compressive-sensing-based image-hiding methods,the contributions of AiEhS are as follows:(1)A new scheme is designed using an auto-encoder to compress the SPI,which can reduce the time cost of both compression and reconstruction,thus accelerating the execution efficiency.(2)The scrambled image is hidden in a carrier image by RNN,which can increase the embedding amount and achieve better hiding quality.Furthermore,experiments show that AiEhS using deep learning can ensure better security and efficiency for image encryption and hiding,in contrast with the traditional image compression and embedding technique.For example,the peak signal-to-noise ratio for the reconstructed image exceeds 34 d B.
