The growing complexity of cyber threats requires innovative machine learning techniques,and image-based malware classification opens up new possibilities.Meanwhile,existing research has largely overlooked the impact o...The growing complexity of cyber threats requires innovative machine learning techniques,and image-based malware classification opens up new possibilities.Meanwhile,existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection,and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges.This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis.The proposed methodology addresses existing classification limitations by introducing noise addition,which simulates obfuscated malware,and denoising strategies to restore robust image representations.To evaluate the approach,we used multiple CNN-based classifiers to assess noise resistance across architectures and datasets,measuring significant performance variation.Our denoising technique demonstrates remarkable performance improvements across two multi-class public datasets,MALIMG and BIG-15.For example,the MALIMG classification accuracy improved from 23.73%to 88.84%with denoising applied after Gaussian noise injection,demonstrating robustness.This approach contributes to improving malware detection by offering a robust framework for noise-resilient classification in noisy conditions.展开更多
Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies ...Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.展开更多
The processing of personal data gives a rise to many privacy concerns,and one of them is to ensure the transpar-ency of data processing to end users.Usually this information is communicated to them using privacy polic...The processing of personal data gives a rise to many privacy concerns,and one of them is to ensure the transpar-ency of data processing to end users.Usually this information is communicated to them using privacy policies.In this paper,the problem of user notifcation in case of data breaches and policy changes is addressed,besides an ontol-ogy-based approach to model them is proposed.To specify the ontology concepts and properties,the requirements and recommendations for the legislative regulations as well as existing privacy policies are evaluated.A set of SPARQL queries to validate the correctness and completeness of the proposed ontology are developed.The proposed approach is applied to evaluate the privacy policies designed by cloud computing providers and IoT device manu-facturers.The results of the analysis show that the transparency of user notifcation scenarios presented in the privacy policies is still very low,and the companies should reconsider the notifcation mechanisms and provide more detailed information in privacy policies.展开更多
文摘The growing complexity of cyber threats requires innovative machine learning techniques,and image-based malware classification opens up new possibilities.Meanwhile,existing research has largely overlooked the impact of noise and obfuscation techniques commonly employed by malware authors to evade detection,and there is a critical gap in using noise simulation as a means of replicating real-world malware obfuscation techniques and adopting denoising framework to counteract these challenges.This study introduces an image denoising technique based on a U-Net combined with a GAN framework to address noise interference and obfuscation challenges in image-based malware analysis.The proposed methodology addresses existing classification limitations by introducing noise addition,which simulates obfuscated malware,and denoising strategies to restore robust image representations.To evaluate the approach,we used multiple CNN-based classifiers to assess noise resistance across architectures and datasets,measuring significant performance variation.Our denoising technique demonstrates remarkable performance improvements across two multi-class public datasets,MALIMG and BIG-15.For example,the MALIMG classification accuracy improved from 23.73%to 88.84%with denoising applied after Gaussian noise injection,demonstrating robustness.This approach contributes to improving malware detection by offering a robust framework for noise-resilient classification in noisy conditions.
基金funded by Natural Science Foundation of Heilongjiang Province,grant number LH2023F020.
文摘Deep learning(DL)has revolutionized time series forecasting(TSF),surpassing traditional statistical methods(e.g.,ARIMA)and machine learning techniques in modeling complex nonlinear dynamics and long-term dependencies prevalent in real-world temporal data.This comprehensive survey reviews state-of-the-art DL architectures forTSF,focusing on four core paradigms:(1)ConvolutionalNeuralNetworks(CNNs),adept at extracting localized temporal features;(2)Recurrent Neural Networks(RNNs)and their advanced variants(LSTM,GRU),designed for sequential dependency modeling;(3)Graph Neural Networks(GNNs),specialized for forecasting structured relational data with spatial-temporal dependencies;and(4)Transformer-based models,leveraging self-attention mechanisms to capture global temporal patterns efficiently.We provide a rigorous analysis of the theoretical underpinnings,recent algorithmic advancements(e.g.,TCNs,attention mechanisms,hybrid architectures),and practical applications of each framework,supported by extensive benchmark datasets(e.g.,ETT,traffic flow,financial indicators)and standardized evaluation metrics(MAE,MSE,RMSE).Critical challenges,including handling irregular sampling intervals,integrating domain knowledge for robustness,and managing computational complexity,are thoroughly discussed.Emerging research directions highlighted include diffusion models for uncertainty quantification,hybrid pipelines combining classical statistical and DL techniques for enhanced interpretability,quantile regression with Transformers for riskaware forecasting,and optimizations for real-time deployment.This work serves as an essential reference,consolidating methodological innovations,empirical resources,and future trends to bridge the gap between theoretical research and practical implementation needs for researchers and practitioners in the field.
文摘The processing of personal data gives a rise to many privacy concerns,and one of them is to ensure the transpar-ency of data processing to end users.Usually this information is communicated to them using privacy policies.In this paper,the problem of user notifcation in case of data breaches and policy changes is addressed,besides an ontol-ogy-based approach to model them is proposed.To specify the ontology concepts and properties,the requirements and recommendations for the legislative regulations as well as existing privacy policies are evaluated.A set of SPARQL queries to validate the correctness and completeness of the proposed ontology are developed.The proposed approach is applied to evaluate the privacy policies designed by cloud computing providers and IoT device manu-facturers.The results of the analysis show that the transparency of user notifcation scenarios presented in the privacy policies is still very low,and the companies should reconsider the notifcation mechanisms and provide more detailed information in privacy policies.
