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
Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabele...Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.展开更多
A new efficient adapting virtual intermediate instruction set,V-IIS,is designed and implemented towards the optimized dynamic binary translator (DBT) system.With the help of this powerful but previously little-studied...A new efficient adapting virtual intermediate instruction set,V-IIS,is designed and implemented towards the optimized dynamic binary translator (DBT) system.With the help of this powerful but previously little-studied component,DBTs can not only get rid of the dependence of machine(s),but also get better performance.From our systematical study and evaluation,experimental results demonstrate that if V-IIS is well designed,without affecting the other optimizing measures,this could make DBT's performance close to those who do not have intermediate instructions.This study is an important step towards the grand goal of high performance "multi-source" and "multi-target" dynamic binary translation.展开更多
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Project (20121101004) supported by the Major Science and Technology Program of Shanxi Province,ChinaProject (20130321004-01) supported by the Key Technologies R&D Program of Shanxi Province,China+2 种基金Project (2013M530896) supported by the Postdoctoral Science Foundation of ChinaProject (2014021022-6) supported by the Shanxi Provincial Science Foundation for Youths,ChinaProject (80010302010053) supported by the Shanxi Characteristic Discipline Fund,China
文摘Ensemble learning is a wildly concerned issue.Traditional ensemble techniques are always adopted to seek better results with labeled data and base classifiers.They fail to address the ensemble task where only unlabeled data are available.A label propagation based ensemble(LPBE) approach is proposed to further combine base classification results with unlabeled data.First,a graph is constructed by taking unlabeled data as vertexes,and the weights in the graph are calculated by correntropy function.Average prediction results are gained from base classifiers,and then propagated under a regularization framework and adaptively enhanced over the graph.The proposed approach is further enriched when small labeled data are available.The proposed algorithms are evaluated on several UCI benchmark data sets.Results of simulations show that the proposed algorithms achieve satisfactory performance compared with existing ensemble methods.
基金Projects(12R21414600)supported by Shanghai Municipal Science and Technology Commission,China
文摘A new efficient adapting virtual intermediate instruction set,V-IIS,is designed and implemented towards the optimized dynamic binary translator (DBT) system.With the help of this powerful but previously little-studied component,DBTs can not only get rid of the dependence of machine(s),but also get better performance.From our systematical study and evaluation,experimental results demonstrate that if V-IIS is well designed,without affecting the other optimizing measures,this could make DBT's performance close to those who do not have intermediate instructions.This study is an important step towards the grand goal of high performance "multi-source" and "multi-target" dynamic binary translation.
