Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during train...Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.展开更多
Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection me...Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.展开更多
Recent works in Unsupervised Domain Adaptation mainly focus on either divergence-based or adversarial methods.Divergence-based approaches minimize domain discrepancy by selecting an appropriate divergence measure,alth...Recent works in Unsupervised Domain Adaptation mainly focus on either divergence-based or adversarial methods.Divergence-based approaches minimize domain discrepancy by selecting an appropriate divergence measure,although the optimal choice can be task-specific in practice.On the other hand,adversarial methods aim to extract domain-invariant features by enforcing indistinguishability between domains in a Min-Max adversarial framework,neglecting the sample correlations.To overcome this limitation,we propose a novel adversarial domain adaptation framework that leverages the collective assumption to model and exploit higher-order interactions among samples.By capturing these collective domain features,our method achieves a more robust domain alignment,demonstrating enhanced resilience to noise and domain ambiguity.Furthermore,experimental results demonstrate that our approach achieves consistent improvements over conventional adversarial training techniques and can seamlessly integrate with existing domain adaptation strategies in a plug-and-play manner,offering a valuable contribution towards advancing state-of-the-art performance.展开更多
Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA...Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA-Spearan automatic phishing detection model utilizing adversarial domain adaptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection.The model effectively identifies phishing behavior in new chains with limited reliable labels,addressing challenges such as significant distribution drift,low attribute overlap,and limited inter-chain connections.Our approach includes a subgraph construction strategy to align heterogeneous chains,a layered deep learning encoder capturing both temporal and spatial information,and integrated adversarial domain adaptive learning in end-to-end model training.Validation in Ethereum,Bitcoin,and EOSIO environments demonstrates ADA-Spear’s effectiveness,achieving an average F1 score of 77.41 on new chains after knowledge transfer,surpassing existing detection methods.展开更多
Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displ...Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.展开更多
Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these prob...Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these problems,we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning.Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain.A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model,while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum,mitigating the inherent heterogeneity between local data.Our experiments are conducted on the largest domain adaptation dataset,and the results show that compared with other traditional federated domain adaptation algorithms,the algorithm we proposed trains a more accurate model,requires fewer communication rounds,makes more effective use of imbalanced data in the industrial area,and protects data privacy.展开更多
<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy i...<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images. </div>展开更多
Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,whi...Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,which adapts the pre-trained model to the target domain.In real scenarios,the availability of labels for target data is rare thus resulting in unsupervised domain adaptation.Herein,we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks(GANs)are integrated to improve the performance of computer vision or robotic vision-based systems in our study.Cosine Generative Adversarial Network(CosGAN)is developed as a GAN that uses cosine embedding loss to handle issues associated with unsupervised source-relax domain adaptations.For less complex architecture,the CosGAN training process has two steps that produce results almost comparable to other state-of-the-art techniques.The efficiency of CosGAN was compared by conducting experiments using benchmarked datasets.The approach was evaluated on different datasets and experimental results show superiority over existing state-of-the-art methods in terms of accuracy as well as generalization ability.This technique has numerous applications including wheeled robots,autonomous vehicles,warehouse automation,and all image-processing-based automation tasks so it can reshape the field of robotic vision with its ability to make robots adapt to new tasks and environments efficiently without requiring additional labeled data.It lays the groundwork for future expansions in robotic vision and applications.Although GAN provides a variety of outstanding features,it also increases the risk of instability and over-fitting of the training data thus making the data difficult to converge.展开更多
心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域...心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域特征学习的循环一致性生成对抗网络(cycle-consistent generative adversavial network based on spatial-frequency domain feature learning,SFFL-CycleGAN).研究结果表明,该网络无须人为制作配对数据集,增强后的CMR图像组织纹理细节丰富,在结构相似度(structural similarity,SSIM)和峰值信噪比(peak signal to noise ratio,PSNR)等方面均优于现有的配对训练网络以及原始的CycleGAN网络,图像增强效果好,有效助力病情诊断.展开更多
基金the Aerospace Science and Technology Foundation(No.20115557007)the National Natural Science Foundation of China(No.61673262)the Military Science and Technology Foundation of China(No.18-H863-03-ZT-001-006-06)
文摘Domain adaptation and adversarial networks are two main approaches for transfer learning.Domain adaptation methods match the mean values of source and target domains,which requires a very large batch size during training.However,adversarial networks are usually unstable when training.In this paper,we propose a joint method of feature matching and adversarial networks to reduce domain discrepancy and mine domaininvariant features from the local and global aspects.At the same time,our method improves the stability of training.Moreover,the method is embedded into a unified convolutional neural network that can be easily optimized by gradient descent.Experimental results show that our joint method can yield the state-of-the-art results on three common public datasets.
基金the Deanship of Scientific Research at King Khalid University for funding this work through large group under grant number(GRP.2/663/46).
文摘Domain Generation Algorithms(DGAs)continue to pose a significant threat inmodernmalware infrastructures by enabling resilient and evasive communication with Command and Control(C&C)servers.Traditional detection methods-rooted in statistical heuristics,feature engineering,and shallow machine learning-struggle to adapt to the increasing sophistication,linguistic mimicry,and adversarial variability of DGA variants.The emergence of Large Language Models(LLMs)marks a transformative shift in this landscape.Leveraging deep contextual understanding,semantic generalization,and few-shot learning capabilities,LLMs such as BERT,GPT,and T5 have shown promising results in detecting both character-based and dictionary-based DGAs,including previously unseen(zeroday)variants.This paper provides a comprehensive and critical review of LLM-driven DGA detection,introducing a structured taxonomy of LLM architectures,evaluating the linguistic and behavioral properties of benchmark datasets,and comparing recent detection frameworks across accuracy,latency,robustness,and multilingual performance.We also highlight key limitations,including challenges in adversarial resilience,model interpretability,deployment scalability,and privacy risks.To address these gaps,we present a forward-looking research roadmap encompassing adversarial training,model compression,cross-lingual benchmarking,and real-time integration with SIEM/SOAR platforms.This survey aims to serve as a foundational resource for advancing the development of scalable,explainable,and operationally viable LLM-based DGA detection systems.
文摘Recent works in Unsupervised Domain Adaptation mainly focus on either divergence-based or adversarial methods.Divergence-based approaches minimize domain discrepancy by selecting an appropriate divergence measure,although the optimal choice can be task-specific in practice.On the other hand,adversarial methods aim to extract domain-invariant features by enforcing indistinguishability between domains in a Min-Max adversarial framework,neglecting the sample correlations.To overcome this limitation,we propose a novel adversarial domain adaptation framework that leverages the collective assumption to model and exploit higher-order interactions among samples.By capturing these collective domain features,our method achieves a more robust domain alignment,demonstrating enhanced resilience to noise and domain ambiguity.Furthermore,experimental results demonstrate that our approach achieves consistent improvements over conventional adversarial training techniques and can seamlessly integrate with existing domain adaptation strategies in a plug-and-play manner,offering a valuable contribution towards advancing state-of-the-art performance.
基金supported by National Key Research and Development Program of China(Nos.2023YFC3306305,2021YFF0307203,2019QY1300)Foundation Strengthening Program Technical Area Fund(No.2021-JCJQJJ-0908)+4 种基金technological project funding of the State Grid Corporation of China(Contract Number:SG270000YXJS2311060)Youth Innovation Promotion Association CAS(No.2021156)the Strategic Priority Research Program of Chinese Academy of Sciences(No.XDC02040100)National Natural Science Foundation of China(No.61802404)supported by the Program of Key Laboratory of Network Assessment Technology,the Chinese Academy of Sciences,Program of Beijing Key Laboratory of Network Security and Protection Technology.
文摘Despite the growing attention on blockchain,phishing activities have surged,particularly on newly established chains.Acknowledging the challenge of limited intelligence in the early stages of new chains,we propose ADA-Spearan automatic phishing detection model utilizing adversarial domain adaptive learning which symbolizes the method’s ability to penetrate various heterogeneous blockchains for phishing detection.The model effectively identifies phishing behavior in new chains with limited reliable labels,addressing challenges such as significant distribution drift,low attribute overlap,and limited inter-chain connections.Our approach includes a subgraph construction strategy to align heterogeneous chains,a layered deep learning encoder capturing both temporal and spatial information,and integrated adversarial domain adaptive learning in end-to-end model training.Validation in Ethereum,Bitcoin,and EOSIO environments demonstrates ADA-Spear’s effectiveness,achieving an average F1 score of 77.41 on new chains after knowledge transfer,surpassing existing detection methods.
基金supported by the National Natural Science Foundation of China(Grant No.51674169)Department of Education of Hebei Province of China(Grant No.ZD2019140)+1 种基金Natural Science Foundation of Hebei Province of China(Grant No.F2019210243)S&T Program of Hebei(Grant No.22375413D)School of Electrical and Electronics Engineering。
文摘Accurate displacement prediction is critical for the early warning of landslides.The complexity of the coupling relationship between multiple influencing factors and displacement makes the accurate prediction of displacement difficult.Moreover,in engineering practice,insufficient monitoring data limit the performance of prediction models.To alleviate this problem,a displacement prediction method based on multisource domain transfer learning,which helps accurately predict data in the target domain through the knowledge of one or more source domains,is proposed.First,an optimized variational mode decomposition model based on the minimum sample entropy is used to decompose the cumulative displacement into the trend,periodic,and stochastic components.The trend component is predicted by an autoregressive model,and the periodic component is predicted by the long short-term memory.For the stochastic component,because it is affected by uncertainties,it is predicted by a combination of a Wasserstein generative adversarial network and multisource domain transfer learning for improved prediction accuracy.Considering a real mine slope as a case study,the proposed prediction method was validated.Therefore,this study provides new insights that can be applied to scenarios lacking sample data.
基金Supported by the Scientific and Technological Innovation 2030—Major Project of"New Generation Artificial Intelligence"(2020AAA0109300)。
文摘Smart manufacturing suffers from the heterogeneity of local data distribution across parties,mutual information silos and lack of privacy protection in the process of industry chain collaboration.To address these problems,we propose a federated domain adaptation algorithm based on knowledge distillation and contrastive learning.Knowledge distillation is used to extract transferable integration knowledge from the different source domains and the quality of the extracted integration knowledge is used to assign reasonable weights to each source domain.A more rational weighted average aggregation is used in the aggregation phase of the center server to optimize the global model,while the local model of the source domain is trained with the help of contrastive learning to constrain the local model optimum towards the global model optimum,mitigating the inherent heterogeneity between local data.Our experiments are conducted on the largest domain adaptation dataset,and the results show that compared with other traditional federated domain adaptation algorithms,the algorithm we proposed trains a more accurate model,requires fewer communication rounds,makes more effective use of imbalanced data in the industrial area,and protects data privacy.
文摘<div style="text-align:justify;"> Most existing image dehazing methods based learning are less able to perform well to real hazy images. An important reason is that they are trained on synthetic hazy images whose distribution is different from real hazy images. To relieve this issue, this paper proposes a new hazy scene generation model based on domain adaptation, which uses a variational autoencoder to encode the synthetic hazy image pairs and the real hazy images into the latent space to adapt. The synthetic hazy image pairs guide the model to learn the mapping of clear images to hazy images, the real hazy images are used to adapt the synthetic hazy images’ latent space to real hazy images through generative adversarial loss, so as to make the generative hazy images’ distribution as close to the real hazy images’ distribution as possible. By comparing the results of the model with traditional physical scattering models and Adobe Lightroom CC software, the hazy images generated in this paper is more realistic. Our end-to-end domain adaptation model is also very convenient to synthesize hazy images without depth map. Using traditional method to dehaze the synthetic hazy images generated by this paper, both SSIM and PSNR have been improved, proved that the effectiveness of our method. The non-reference haze density evaluation algorithm and other quantitative evaluation also illustrate the advantages of our method in synthetic hazy images. </div>
文摘Domain shift is when the data used in training does not match the ones it will be applied to later on under similar conditions.Domain shift will reduce accuracy in results.To prevent this,domain adaptation is done,which adapts the pre-trained model to the target domain.In real scenarios,the availability of labels for target data is rare thus resulting in unsupervised domain adaptation.Herein,we propose an innovative approach where source-free domain adaptation models and Generative Adversarial Networks(GANs)are integrated to improve the performance of computer vision or robotic vision-based systems in our study.Cosine Generative Adversarial Network(CosGAN)is developed as a GAN that uses cosine embedding loss to handle issues associated with unsupervised source-relax domain adaptations.For less complex architecture,the CosGAN training process has two steps that produce results almost comparable to other state-of-the-art techniques.The efficiency of CosGAN was compared by conducting experiments using benchmarked datasets.The approach was evaluated on different datasets and experimental results show superiority over existing state-of-the-art methods in terms of accuracy as well as generalization ability.This technique has numerous applications including wheeled robots,autonomous vehicles,warehouse automation,and all image-processing-based automation tasks so it can reshape the field of robotic vision with its ability to make robots adapt to new tasks and environments efficiently without requiring additional labeled data.It lays the groundwork for future expansions in robotic vision and applications.Although GAN provides a variety of outstanding features,it also increases the risk of instability and over-fitting of the training data thus making the data difficult to converge.
文摘心脏磁共振成像(cardiac magnetic resonance,CMR)过程中患者误动、异常幅度的呼吸运动、心律失常会造成CMR图像质量下降,为解决现有的CMR图像增强网络需要人为制作配对数据,且图像增强后部分组织纹理细节丢失的问题,提出了基于空频域特征学习的循环一致性生成对抗网络(cycle-consistent generative adversavial network based on spatial-frequency domain feature learning,SFFL-CycleGAN).研究结果表明,该网络无须人为制作配对数据集,增强后的CMR图像组织纹理细节丰富,在结构相似度(structural similarity,SSIM)和峰值信噪比(peak signal to noise ratio,PSNR)等方面均优于现有的配对训练网络以及原始的CycleGAN网络,图像增强效果好,有效助力病情诊断.
