Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodolo...Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.展开更多
The widespread usage of Cyber Physical Systems(CPSs)generates a vast volume of time series data,and precisely determining anomalies in the data is critical for practical production.Autoencoder is the mainstream method...The widespread usage of Cyber Physical Systems(CPSs)generates a vast volume of time series data,and precisely determining anomalies in the data is critical for practical production.Autoencoder is the mainstream method for time series anomaly detection,and the anomaly is judged by reconstruction error.However,due to the strong generalization ability of neural networks,some abnormal samples close to normal samples may be judged as normal,which fails to detect the abnormality.In addition,the dataset rarely provides sufficient anomaly labels.This research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series to solve the above problem.Firstly,an encoder encodes the input data into low-dimensional space to acquire a feature vector.Then,a memory module is used to learn the feature vector’s prototype patterns and update the feature vectors.The updating process allows partial forgetting of information to prevent model overgeneralization.After that,two decoders reconstruct the input data.Finally,this research uses the Peak Over Threshold(POT)method to calculate the threshold to determine anomalous samples from normal samples.This research uses a two-stage adversarial training strategy during model training to enlarge the gap between the reconstruction error of normal and abnormal samples.The proposed method achieves significant anomaly detection results on synthetic and real datasets from power systems,water treatment plants,and computer clusters.The F1 score reached an average of 0.9196 on the five datasets,which is 0.0769 higher than the best baseline method.展开更多
Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from n...Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.展开更多
Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS...Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.展开更多
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological struct...Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed.展开更多
Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence i...Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.展开更多
The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS ...The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.展开更多
The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the rob...The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.展开更多
为有效解决多维时间序列(multivariate time series, MTS)无监督异常检测模型中自编码器模块容易拟合异常样本、正常MTS样本对应的隐空间特征可能被重构为异常MTS的问题,设计一种具有三重生成对抗的MTS异常检测模型。以LSTM自编码器为...为有效解决多维时间序列(multivariate time series, MTS)无监督异常检测模型中自编码器模块容易拟合异常样本、正常MTS样本对应的隐空间特征可能被重构为异常MTS的问题,设计一种具有三重生成对抗的MTS异常检测模型。以LSTM自编码器为生成器,基于重构误差生成伪标签,由判别器区分经伪标签过滤后的重构MTS和原始MTS;采用两次对抗训练将LSTM自编码器的隐空间约束为均匀分布,减少LSTM自编码器隐空间特征重构出异常MTS的可能性。多个公开MTS数据集上的实验结果表明,T-GAN能在带有污染数据的训练集上更好学习正常MTS分布,取得较高的异常检测效果。展开更多
【目的】设计一种基于FIML和DAE的填充缺失值的方法,即聚类全信息选择性过滤编码器数据填补算法(clustering-based comprehensive information selective filtering encoder data imputation algorithm,CFSM-DAE),为水稻种质资源缺失数...【目的】设计一种基于FIML和DAE的填充缺失值的方法,即聚类全信息选择性过滤编码器数据填补算法(clustering-based comprehensive information selective filtering encoder data imputation algorithm,CFSM-DAE),为水稻种质资源缺失数据进行填充。【方法】利用聚类辅助避免数据异常值对算法的影响,采用选择性过滤层用于识别高质量估算、减少低质量估算的影响。传统的DAE框架通常没有选择性过滤层,所有的估算值都被视为同等重要,无法区分高质量和低质量的估算值。为了进一步提高估算精度,研究采用集成框架将全信息最大似然性(FIML)与多对抗性自编码器(DAE)结合的方法(CFSM-DAE),在选择性过滤层基础上,自适应填充,即当估算值不符合设定阈值时,采用FIML填充策略以确保填充结果的稳定性和精确度,从而进一步来提高整体估算精度。在3种缺失数据机制(随机缺失(MAR)、完全随机缺失(MCAR)和非随机缺失(MNAR))下对模拟数据和实际水稻种质资源数据集进行研究,将CFSM-DAE方法与多种常用填充算法比较(全信息最大似然性(FIML)、对抗自编码器(DAE)、K近邻填充(KNN)、随机森林(RF)、链式方程多重插补(MICE))。【结果】CFSM-DAE在模拟数据上的表现为S_(RME)=0.0676,E_(MA)=0.0093,R^(2)=0.9958;在水稻种质资源数据上的表现为S_(RME)=0.0395,E_(MA)=0.0078,R^(2)=0.8913。相比之下,其他算法如DAE在这两类数据下的SRME表现分别为0.8896和0.7707;KNN算法的EMA表现分别为0.1183和0.1305;FIML算法的R2表现为0.3382和0.7321。因此,CFSM-DAE在多个评价指标上相较于其他算法都表现出了一定的提升,CFSM-DAE在模拟数据和水稻种质资源数据的表现优于其他算法。【结论】CFSM-DAE方法通过结合聚类、选择性过滤和全信息最大似然性等策略,显著提高了水稻种质资源数据中缺失值的填补精度,展示了其在处理复杂缺失值问题上的有效性和潜力。展开更多
基金supported by“Regional Innovation Strategy(RIS)”through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(MOE)(2021RIS-002)the Technology Development Program(RS-2023-00266141)funded by the Ministry of SMEs and Startups(MSS,Republic of Korea).
文摘Virtual Power Plants(VPPs)are integral to modern energy systems,providing stability and reliability in the face of the inherent complexities and fluctuations of solar power data.Traditional anomaly detection methodologies often need to adequately handle these fluctuations from solar radiation and ambient temperature variations.We introduce the Memory-Enhanced Autoencoder with Adversarial Training(MemAAE)model to overcome these limitations,designed explicitly for robust anomaly detection in VPP environments.The MemAAE model integrates three principal components:an LSTM-based autoencoder that effectively captures temporal dynamics to distinguish between normal and anomalous behaviors,an adversarial training module that enhances system resilience across diverse operational scenarios,and a prediction module that aids the autoencoder during the reconstruction process,thereby facilitating precise anomaly identification.Furthermore,MemAAE features a memory mechanism that stores critical pattern information,mitigating overfitting,alongside a dynamic threshold adjustment mechanism that adapts detection thresholds in response to evolving operational conditions.Our empirical evaluation of the MemAAE model using real-world solar power data shows that the model outperforms other comparative models on both datasets.On the Sopan-Finder dataset,MemAAE has an accuracy of 99.17%and an F1-score of 95.79%,while on the Sunalab Faro PV 2017 dataset,it has an accuracy of 97.67%and an F1-score of 93.27%.Significant performance advantages have been achieved on both datasets.These results show that MemAAE model is an effective method for real-time anomaly detection in virtual power plants(VPPs),which can enhance robustness and adaptability to inherent variables in solar power generation.
基金supported by the National Natural Science Foundation of China(62203431)。
文摘The widespread usage of Cyber Physical Systems(CPSs)generates a vast volume of time series data,and precisely determining anomalies in the data is critical for practical production.Autoencoder is the mainstream method for time series anomaly detection,and the anomaly is judged by reconstruction error.However,due to the strong generalization ability of neural networks,some abnormal samples close to normal samples may be judged as normal,which fails to detect the abnormality.In addition,the dataset rarely provides sufficient anomaly labels.This research proposes an unsupervised anomaly detection approach based on adversarial memory autoencoders for multivariate time series to solve the above problem.Firstly,an encoder encodes the input data into low-dimensional space to acquire a feature vector.Then,a memory module is used to learn the feature vector’s prototype patterns and update the feature vectors.The updating process allows partial forgetting of information to prevent model overgeneralization.After that,two decoders reconstruct the input data.Finally,this research uses the Peak Over Threshold(POT)method to calculate the threshold to determine anomalous samples from normal samples.This research uses a two-stage adversarial training strategy during model training to enlarge the gap between the reconstruction error of normal and abnormal samples.The proposed method achieves significant anomaly detection results on synthetic and real datasets from power systems,water treatment plants,and computer clusters.The F1 score reached an average of 0.9196 on the five datasets,which is 0.0769 higher than the best baseline method.
文摘Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.
基金supported by the MSIT (Ministry of Science and ICT),Korea,under the ITRC (Information Technology Research Center)support program (IITP-2018-0-01405)supervised by the IITP (Institute for Information&Communications Technology Planning&Evaluation).
文摘Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
基金Supported by the Strategy Priority Research Program of Chinese Academy of Sciences(No.XDC02070600).
文摘Graph embedding aims to map the high-dimensional nodes to a low-dimensional space and learns the graph relationship from its latent representations.Most existing graph embedding methods focus on the topological structure of graph data,but ignore the semantic information of graph data,which results in the unsatisfied performance in practical applications.To overcome the problem,this paper proposes a novel deep convolutional adversarial graph autoencoder(GAE)model.To embed the semantic information between nodes in the graph data,the random walk strategy is first used to construct the positive pointwise mutual information(PPMI)matrix,then,graph convolutional net-work(GCN)is employed to encode the PPMI matrix and node content into the latent representation.Finally,the learned latent representation is used to reconstruct the topological structure of the graph data by decoder.Furthermore,the deep convolutional adversarial training algorithm is introduced to make the learned latent representation conform to the prior distribution better.The state-of-the-art experimental results on the graph data validate the effectiveness of the proposed model in the link prediction,node clustering and graph visualization tasks for three standard datasets,Cora,Citeseer and Pubmed.
文摘Many types of real-world information systems, including social media and e-commerce platforms, can be modelled by means of attribute-rich, connected networks. The goal of anomaly detection in artificial intelligence is to identify illustrations that deviate significantly from the main distribution of data or that differ from known cases. Anomalous nodes in node-attributed networks can be identified with greater precision if both graph and node attributes are taken into account. Almost all of the studies in this area focus on supervised techniques for spotting outliers. While supervised algorithms for anomaly detection work well in theory, they cannot be applied to real-world applications owing to a lack of labelled data. Considering the possible data distribution, our model employs a dual variational autoencoder (VAE), while a generative adversarial network (GAN) assures that the model is robust to adversarial training. The dual VAEs are used in another capacity: as a fake-node generator. Adversarial training is used to ensure that our latent codes have a Gaussian or uniform distribution. To provide a fair presentation of the graph, the discriminator instructs the generator to generate latent variables with distributions that are more consistent with the actual distribution of the data. Once the model has been learned, the discriminator is used for anomaly detection via reconstruction loss which has been trained to distinguish between the normal and artificial distributions of data. First, using a dual VAE, our model simultaneously captures cross-modality interactions between topological structure and node characteristics and overcomes the problem of unlabeled anomalies, allowing us to better understand the network sparsity and nonlinearity. Second, the proposed model considers the regularization of the latent codes while solving the issue of unregularized embedding techniques that can quickly lead to unsatisfactory representation. Finally, we use the discriminator reconstruction loss for anomaly detection as the discriminator is well-trained to separate the normal and generated data distributions because reconstruction-based loss does not include the adversarial component. Experiments conducted on attributed networks demonstrate the effectiveness of the proposed model and show that it greatly surpasses the previous methods. The area under the curve scores of our proposed model for the BlogCatalog, Flickr, and Enron datasets are 0.83680, 0.82020, and 0.71180, respectively, proving the effectiveness of the proposed model. The result of the proposed model on the Enron dataset is slightly worse than other models;we attribute this to the dataset’s low dimensionality as the most probable explanation.
基金the Deanship of Research and Graduate Studies at King Khalid University for funding this work through Large Group Project under grant number(RGP.2/245/46)funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R760)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.The research team thanks the Deanship of Graduate Studies and Scientific Research at Najran University for supporting the research project through the Nama’a program,with the project code NU/GP/SERC/13/352-1。
文摘The emergence of Generative Adversarial Network(GAN)techniques has garnered significant attention from the research community for the development of Intrusion Detection Systems(IDS).However,conventional GAN-based IDS models face several challenges,including training instability,high computational costs,and system failures.To address these limitations,we propose a Hybrid Wasserstein GAN and Autoencoder Model(WGAN-AE)for intrusion detection.The proposed framework leverages the stability of WGAN and the feature extraction capabilities of the Autoencoder Model.The model was trained and evaluated using two recent benchmark datasets,5GNIDD and IDSIoT2024.When trained on the 5GNIDD dataset,the model achieved an average area under the precisionrecall curve is 99.8%using five-fold cross-validation and demonstrated a high detection accuracy of 97.35%when tested on independent test data.Additionally,the model is well-suited for deployment on resource-limited Internetof-Things(IoT)devices due to its ability to detect attacks within microseconds and its small memory footprint of 60.24 kB.Similarly,when trained on the IDSIoT2024 dataset,the model achieved an average PR-AUC of 94.09%and an attack detection accuracy of 97.35%on independent test data,with a memory requirement of 61.84 kB.Extensive simulation results demonstrate that the proposed hybrid model effectively addresses the shortcomings of traditional GAN-based IDS approaches in terms of detection accuracy,computational efficiency,and applicability to real-world IoT environments.
基金the National Nat-ural Science Foundation of China under Grant No.62072406,No.U19B2016,No.U20B2038 and No.61871398the Natural Science Foundation of Zhejiang Province under Grant No.LY19F020025the Major Special Funding for“Science and Tech-nology Innovation 2025”in Ningbo under Grant No.2018B10063.
文摘The spectrum sensing model based on deep learning has achieved satisfying detection per-formence,but its robustness has not been verified.In this paper,we propose primary user adversarial attack(PUAA)to verify the robustness of the deep learning based spectrum sensing model.PUAA adds a care-fully manufactured perturbation to the benign primary user signal,which greatly reduces the probability of detection of the spectrum sensing model.We design three PUAA methods in black box scenario.In or-der to defend against PUAA,we propose a defense method based on autoencoder named DeepFilter.We apply the long short-term memory network and the convolutional neural network together to DeepFilter,so that it can extract the temporal and local features of the input signal at the same time to achieve effective defense.Extensive experiments are conducted to eval-uate the attack effect of the designed PUAA method and the defense effect of DeepFilter.Results show that the three PUAA methods designed can greatly reduce the probability of detection of the deep learning-based spectrum sensing model.In addition,the experimen-tal results of the defense effect of DeepFilter show that DeepFilter can effectively defend against PUAA with-out affecting the detection performance of the model.
文摘为有效解决多维时间序列(multivariate time series, MTS)无监督异常检测模型中自编码器模块容易拟合异常样本、正常MTS样本对应的隐空间特征可能被重构为异常MTS的问题,设计一种具有三重生成对抗的MTS异常检测模型。以LSTM自编码器为生成器,基于重构误差生成伪标签,由判别器区分经伪标签过滤后的重构MTS和原始MTS;采用两次对抗训练将LSTM自编码器的隐空间约束为均匀分布,减少LSTM自编码器隐空间特征重构出异常MTS的可能性。多个公开MTS数据集上的实验结果表明,T-GAN能在带有污染数据的训练集上更好学习正常MTS分布,取得较高的异常检测效果。
文摘【目的】设计一种基于FIML和DAE的填充缺失值的方法,即聚类全信息选择性过滤编码器数据填补算法(clustering-based comprehensive information selective filtering encoder data imputation algorithm,CFSM-DAE),为水稻种质资源缺失数据进行填充。【方法】利用聚类辅助避免数据异常值对算法的影响,采用选择性过滤层用于识别高质量估算、减少低质量估算的影响。传统的DAE框架通常没有选择性过滤层,所有的估算值都被视为同等重要,无法区分高质量和低质量的估算值。为了进一步提高估算精度,研究采用集成框架将全信息最大似然性(FIML)与多对抗性自编码器(DAE)结合的方法(CFSM-DAE),在选择性过滤层基础上,自适应填充,即当估算值不符合设定阈值时,采用FIML填充策略以确保填充结果的稳定性和精确度,从而进一步来提高整体估算精度。在3种缺失数据机制(随机缺失(MAR)、完全随机缺失(MCAR)和非随机缺失(MNAR))下对模拟数据和实际水稻种质资源数据集进行研究,将CFSM-DAE方法与多种常用填充算法比较(全信息最大似然性(FIML)、对抗自编码器(DAE)、K近邻填充(KNN)、随机森林(RF)、链式方程多重插补(MICE))。【结果】CFSM-DAE在模拟数据上的表现为S_(RME)=0.0676,E_(MA)=0.0093,R^(2)=0.9958;在水稻种质资源数据上的表现为S_(RME)=0.0395,E_(MA)=0.0078,R^(2)=0.8913。相比之下,其他算法如DAE在这两类数据下的SRME表现分别为0.8896和0.7707;KNN算法的EMA表现分别为0.1183和0.1305;FIML算法的R2表现为0.3382和0.7321。因此,CFSM-DAE在多个评价指标上相较于其他算法都表现出了一定的提升,CFSM-DAE在模拟数据和水稻种质资源数据的表现优于其他算法。【结论】CFSM-DAE方法通过结合聚类、选择性过滤和全信息最大似然性等策略,显著提高了水稻种质资源数据中缺失值的填补精度,展示了其在处理复杂缺失值问题上的有效性和潜力。
