On the basis of the objective functions,dithering optimization techniques can be divided into the intensity-based optimization technique and the phase-based optimization technique.However,both types of techniques are ...On the basis of the objective functions,dithering optimization techniques can be divided into the intensity-based optimization technique and the phase-based optimization technique.However,both types of techniques are spatial-domain optimization techniques,while their measurement performances are essentially determined by the harmonic components in the frequency domain.In this paper,a novel genetic optimization technique in the frequency domain is proposed for highquality fringe generation.In addition,to handle the time-consuming difficulty of genetic algorithm(GA),we first optimize a binary patch,then join the optimal binary patches together according to periodicity and symmetry so as to generate a full-size pattern.It is verified that the proposed technique can significantly enhance the measured performance and ensure the robustness to various amounts of defocusing.展开更多
Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learni...Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.展开更多
Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 ser...Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79%for the detection and macro average precision and recall of 83%for the classification task of DGA domain names.展开更多
Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 ser...Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.展开更多
In the new network business,the danger of botnets should not be underestimated.Botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receiv...In the new network business,the danger of botnets should not be underestimated.Botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receive commands from the botmaster,carrying out further attack activities.Therefore,a system based onmachine learning to dichotomizeDNSdomain access is designed,which can instantly detectDGAdomain names and thus quickly dispose of infected computers to avoid spreading the virus and further damage.In the comparison,the bidirectional LSTM model slightly outperformed the unidirectional LSTM network and achieved 99%accuracy in the open dataset classification task.展开更多
基金Project supported by the Science and Technology Major Projects of Zhejiang Province,China(Grant No.2017C31080)
文摘On the basis of the objective functions,dithering optimization techniques can be divided into the intensity-based optimization technique and the phase-based optimization technique.However,both types of techniques are spatial-domain optimization techniques,while their measurement performances are essentially determined by the harmonic components in the frequency domain.In this paper,a novel genetic optimization technique in the frequency domain is proposed for highquality fringe generation.In addition,to handle the time-consuming difficulty of genetic algorithm(GA),we first optimize a binary patch,then join the optimal binary patches together according to periodicity and symmetry so as to generate a full-size pattern.It is verified that the proposed technique can significantly enhance the measured performance and ensure the robustness to various amounts of defocusing.
基金Project supported by the National Natural Science Foundation of China (No. 61071154)the project FP7 "Responsive Open Learning Environments" of European Union
文摘Online learners are individuals,and their learning abilities,knowledge,and learning performance differ substantially and are ever changing.These individual characteristics pose considerable challenges to online learning courses.In this paper,we propose an online course generation and evolution approach based on genetic algorithms to provide personalized learning.The courses generated consider not only the difficulty level of a concept and the time spent by an individual learner on the concept,but also the changing learning performance of the individual learner during the learning process.We present a layered topological sort algorithm,which converges towards an optimal solution while considering multiple objectives.Our general approach makes use of the stochastic convergence of genetic algorithms.Experimental results show that the proposed algorithm is superior to the free browsing learning mode typically enabled by online learning environments because of the precise selection of learning content relevant to the individual learner,which results in good learning performance.
基金Our research was supported by the National Key Research and Development Program of China(Grant No.2016YFB0801004)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDC02030200)the National Key Research and Development Program of China(Grant No.2018YFC0824801).
文摘Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79%for the detection and macro average precision and recall of 83%for the classification task of DGA domain names.
基金supported by the National Key Research and Development Program of China(Grant No.2016YFB0801004)the Strategic Priority Research Program of Chinese Academy of Sciences(Grant No.XDC02030200)the National Key Research and Development Program of China(Grant No.2018YFC0824801).
文摘Command and control(C2)servers are used by attackers to operate communications.To perform attacks,attackers usually employee the Domain Generation Algorithm(DGA),with which to confirm rendezvous points to their C2 servers by generating various network locations.The detection of DGA domain names is one of the important technologies for command and control communication detection.Considering the randomness of the DGA domain names,recent research in DGA detection applyed machine learning methods based on features extracting and deep learning architectures to classify domain names.However,these methods are insufficient to handle wordlist-based DGA threats,which generate domain names by randomly concatenating dictionary words according to a special set of rules.In this paper,we proposed a a deep learning framework ATT-CNN-BiLSTMfor identifying and detecting DGA domains to alleviate the threat.Firstly,the Convolutional Neural Network(CNN)and bidirectional Long Short-Term Memory(BiLSTM)neural network layer was used to extract the features of the domain sequences information;secondly,the attention layer was used to allocate the corresponding weight of the extracted deep information from the domain names.Finally,the different weights of features in domain names were put into the output layer to complete the tasks of detection and classification.Our extensive experimental results demonstrate the effectiveness of the proposed model,both on regular DGA domains and DGA that hard to detect such as wordlist-based and part-wordlist-based ones.To be precise,we got a F1 score of 98.79% for the detection and macro average precision and recall of 83% for the classification task of DGA domain names.
基金Supported by Hainan Provincial National Science Foundation of China,621MS0789.
文摘In the new network business,the danger of botnets should not be underestimated.Botnets often generatemalicious domain names through DGAs to enable communication with command and control servers(C&C)and then receive commands from the botmaster,carrying out further attack activities.Therefore,a system based onmachine learning to dichotomizeDNSdomain access is designed,which can instantly detectDGAdomain names and thus quickly dispose of infected computers to avoid spreading the virus and further damage.In the comparison,the bidirectional LSTM model slightly outperformed the unidirectional LSTM network and achieved 99%accuracy in the open dataset classification task.
