In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through cr...In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.展开更多
Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many...Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many-core)towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm.In the last decade,the graphics processing unit,or GPU,has gained an important place in the field of high performance computing(HPC)because of its low cost and massive parallel processing power.Super-computing has become,for the first time,available to anyone at the price of a desktop computer.In this paper,we survey the concept of parallel computing and especially GPU computing.Achieving efficient parallel algorithms for the GPU is not a trivial task,there are several technical restrictions that must be satisfied in order to achieve the expected performance.Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it.Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model.In particular,we show how this new technology can help the field of computational physics,especially when the problem is data-parallel.We present four examples of computational physics problems;n-body,collision detection,Potts model and cellular automata simulations.These examples well represent the kind of problems that are suitable for GPU computing.By understanding the GPU architecture and its massive parallelism programming model,one can overcome many of the technical limitations found along the way,design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.展开更多
Transaction parallelism in database systems is an attractive way of improving transaction performance. There exists two levels of transaction parallelism, inter-transaction level and intra-transaction level. With the ...Transaction parallelism in database systems is an attractive way of improving transaction performance. There exists two levels of transaction parallelism, inter-transaction level and intra-transaction level. With the advent of multi- core processors, new hopes of improving transaction parallelism appear on the scene. The greatest execution efficiency of concurrent transactions comes from the lowest dependencies of them. However, the dependencies of concurrent transactions stand in the way of exploiting parallelism. In this paper, we present Resource Snapshot Model （RSM） for resource modeling in both levels. We propose a non-restarting scheduling algorithm in the inter-transaction level and a processor assignment algorithm in the intra-transaction level in terms of multi-core processors. Through these algorithms, execution performance of transaction streams will be improved in a parallel system with multiple heterogeneous processors that have different number of cores.展开更多
文摘In the era of the Internet,widely used web applications have become the target of hacker attacks because they contain a large amount of personal information.Among these vulnerabilities,stealing private data through crosssite scripting(XSS)attacks is one of the most commonly used attacks by hackers.Currently,deep learning-based XSS attack detection methods have good application prospects;however,they suffer from problems such as being prone to overfitting,a high false alarm rate,and low accuracy.To address these issues,we propose a multi-stage feature extraction and fusion model for XSS detection based on Random Forest feature enhancement.The model utilizes RandomForests to capture the intrinsic structure and patterns of the data by extracting leaf node indices as features,which are subsequentlymergedwith the original data features to forma feature setwith richer information content.Further feature extraction is conducted through three parallel channels.Channel I utilizes parallel onedimensional convolutional layers(1Dconvolutional layers)with different convolutional kernel sizes to extract local features at different scales and performmulti-scale feature fusion;Channel II employsmaximum one-dimensional pooling layers(max 1D pooling layers)of various sizes to extract key features from the data;and Channel III extracts global information bi-directionally using a Bi-Directional Long-Short TermMemory Network(Bi-LSTM)and incorporates a multi-head attention mechanism to enhance global features.Finally,effective classification and prediction of XSS are performed by fusing the features of the three channels.To test the effectiveness of the model,we conduct experiments on six datasets.We achieve an accuracy of 100%on the UNSW-NB15 dataset and 99.99%on the CICIDS2017 dataset,which is higher than that of the existing models.
基金supported by Fondecyt Project No.1120495.Finally,thanks to Renato Cerro for improving the English of this manuscript.
文摘Parallel computing has become an important subject in the field of computer science and has proven to be critical when researching high performance solutions.The evolution of computer architectures(multi-core and many-core)towards a higher number of cores can only confirm that parallelism is the method of choice for speeding up an algorithm.In the last decade,the graphics processing unit,or GPU,has gained an important place in the field of high performance computing(HPC)because of its low cost and massive parallel processing power.Super-computing has become,for the first time,available to anyone at the price of a desktop computer.In this paper,we survey the concept of parallel computing and especially GPU computing.Achieving efficient parallel algorithms for the GPU is not a trivial task,there are several technical restrictions that must be satisfied in order to achieve the expected performance.Some of these limitations are consequences of the underlying architecture of the GPU and the theoretical models behind it.Our goal is to present a set of theoretical and technical concepts that are often required to understand the GPU and its massive parallelism model.In particular,we show how this new technology can help the field of computational physics,especially when the problem is data-parallel.We present four examples of computational physics problems;n-body,collision detection,Potts model and cellular automata simulations.These examples well represent the kind of problems that are suitable for GPU computing.By understanding the GPU architecture and its massive parallelism programming model,one can overcome many of the technical limitations found along the way,design better GPU-based algorithms for computational physics problems and achieve speedups that can reach up to two orders of magnitude when compared to sequential implementations.
基金supported by the National Natural Science Foundation of China under Grant No.61073061
文摘Transaction parallelism in database systems is an attractive way of improving transaction performance. There exists two levels of transaction parallelism, inter-transaction level and intra-transaction level. With the advent of multi- core processors, new hopes of improving transaction parallelism appear on the scene. The greatest execution efficiency of concurrent transactions comes from the lowest dependencies of them. However, the dependencies of concurrent transactions stand in the way of exploiting parallelism. In this paper, we present Resource Snapshot Model （RSM） for resource modeling in both levels. We propose a non-restarting scheduling algorithm in the inter-transaction level and a processor assignment algorithm in the intra-transaction level in terms of multi-core processors. Through these algorithms, execution performance of transaction streams will be improved in a parallel system with multiple heterogeneous processors that have different number of cores.
