A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete...A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.展开更多
A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to descr...A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.展开更多
DC component is contained in inverter output voltage due to many reasons such as the zero-point deviation of operational amplifiers and the differences between power switching transistors′ characteristics. For the pa...DC component is contained in inverter output voltage due to many reasons such as the zero-point deviation of operational amplifiers and the differences between power switching transistors′ characteristics. For the parallel inverter system without output isolation transformers, the difference of DC components of the output voltage can cause large DC loop-current among modular inverters. Aiming at this problem, this paper studies several DC loop-current detecting and restraining methods. By digital adjustment with high precision on the DC components of reference sine wave, the DC components of inverter′s output voltage can be adjusted to restrain DC loop-current. Experimental results prove that the DC loop-current detecting and restraining methods have a good performance.展开更多
With rapid development of blockchain technology,blockchain and its security theory research and practical application have become crucial.At present,a new DDoS attack has arisen,and it is the DDoS attack in blockchain...With rapid development of blockchain technology,blockchain and its security theory research and practical application have become crucial.At present,a new DDoS attack has arisen,and it is the DDoS attack in blockchain network.The attack is harmful for blockchain technology and many application scenarios.However,the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution.Aiming at the above problem,the paper proposes the virtual reality parallel anti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning.Here,Ada Boost and Random Forest are used as our ensemble learning strategy,and some different lightweight classifiers are integrated into the same ensemble learning algorithm,such as CART and ID3.Our detection framework in blockchain scene has much stronger generalization performance,universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network.Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators(such as Precision,Recall,F-Score,True Positive Rate,False Positive Rate,and ROC curve).展开更多
It is necessary for an MC-CDMA uplink receiver to employ MUD (multiuser detection) in a frequency selective fading channel. After analyzing the algorithm of PIC(parallel interference cancellation) MUD, a novel MUD sch...It is necessary for an MC-CDMA uplink receiver to employ MUD (multiuser detection) in a frequency selective fading channel. After analyzing the algorithm of PIC(parallel interference cancellation) MUD, a novel MUD scheme, Soft-PIC (soft parallel interference cancellation) is proposed. Based on the reliability of each detected user signal in the former stage, this Soft-PIC detection scheme substitutes a soft decision of the variable for the hard decision in PIC scheme. Compared with the PIC scheme, it can reconstruct the interference signals more accurately and eliminate MAI(multiple access interference) in a more efficient way.PIC is one of the most practical schemes in numerous multiuser detection technologies. However, Soft-PIC as an improved PIC scheme deserves further study.展开更多
The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)...The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)measurements.We propose a parallel and optimized genetic Elman network(POGEN)to identify the enrichment of235U based on the physical properties of the measured autocorrelation functions.Theoretical analysis and experimental results indicate that,for 4 different enrichment fissile materials,due to higher information utilization,more efficient network architecture,and optimized parameters,the POGEN-based algorithm can obtain identification results with higher recognition accuracy,compared to the integrated autocorrelation function(IAF)method.展开更多
基金supported by the Key Area R&D Program of Guangdong Province (Grant No.2022B0701180001)the National Natural Science Foundation of China (Grant No.61801127)+1 种基金the Science Technology Planning Project of Guangdong Province,China (Grant Nos.2019B010140002 and 2020B111110002)the Guangdong-Hong Kong-Macao Joint Innovation Field Project (Grant No.2021A0505080006)。
文摘A novel image encryption scheme based on parallel compressive sensing and edge detection embedding technology is proposed to improve visual security. Firstly, the plain image is sparsely represented using the discrete wavelet transform.Then, the coefficient matrix is scrambled and compressed to obtain a size-reduced image using the Fisher–Yates shuffle and parallel compressive sensing. Subsequently, to increase the security of the proposed algorithm, the compressed image is re-encrypted through permutation and diffusion to obtain a noise-like secret image. Finally, an adaptive embedding method based on edge detection for different carrier images is proposed to generate a visually meaningful cipher image. To improve the plaintext sensitivity of the algorithm, the counter mode is combined with the hash function to generate keys for chaotic systems. Additionally, an effective permutation method is designed to scramble the pixels of the compressed image in the re-encryption stage. The simulation results and analyses demonstrate that the proposed algorithm performs well in terms of visual security and decryption quality.
文摘A new real-time model based on parallel time-series mining is proposed to improve the accuracy and efficiency of the network intrusion detection systems. In this model, multidimensional dataset is constructed to describe network events, and sliding window updating algorithm is used to maintain network stream. Moreover, parallel frequent patterns and frequent episodes mining algorithms are applied to implement parallel time-series mining engineer which can intelligently generate rules to distinguish intrusions from normal activities. Analysis and study on the basis of DAWNING 3000 indicate that this parallel time-series mining-based model provides a more accurate and efficient way to building real-time NIDS.
文摘DC component is contained in inverter output voltage due to many reasons such as the zero-point deviation of operational amplifiers and the differences between power switching transistors′ characteristics. For the parallel inverter system without output isolation transformers, the difference of DC components of the output voltage can cause large DC loop-current among modular inverters. Aiming at this problem, this paper studies several DC loop-current detecting and restraining methods. By digital adjustment with high precision on the DC components of reference sine wave, the DC components of inverter′s output voltage can be adjusted to restrain DC loop-current. Experimental results prove that the DC loop-current detecting and restraining methods have a good performance.
基金performed in the Project“Cloud Interaction Technology and Service Platform for Mine Internet of things”supported by National Key Research and Development Program of China(2017YFC0804406)+1 种基金partly supported by the Project“Massive DDoS Attack Traffic Detection Technology Research based on Big Data and Cloud Environment”supported by Scientific Research Foundation of Shandong University of Science and Technology for Recruited Talents(0104060511314)。
文摘With rapid development of blockchain technology,blockchain and its security theory research and practical application have become crucial.At present,a new DDoS attack has arisen,and it is the DDoS attack in blockchain network.The attack is harmful for blockchain technology and many application scenarios.However,the traditional and existing DDoS attack detection and defense means mainly come from the centralized tactics and solution.Aiming at the above problem,the paper proposes the virtual reality parallel anti-DDoS chain design philosophy and distributed anti-D Chain detection framework based on hybrid ensemble learning.Here,Ada Boost and Random Forest are used as our ensemble learning strategy,and some different lightweight classifiers are integrated into the same ensemble learning algorithm,such as CART and ID3.Our detection framework in blockchain scene has much stronger generalization performance,universality and complementarity to identify accurately the onslaught features for DDoS attack in P2P network.Extensive experimental results confirm that our distributed heterogeneous anti-D chain detection method has better performance in six important indicators(such as Precision,Recall,F-Score,True Positive Rate,False Positive Rate,and ROC curve).
文摘It is necessary for an MC-CDMA uplink receiver to employ MUD (multiuser detection) in a frequency selective fading channel. After analyzing the algorithm of PIC(parallel interference cancellation) MUD, a novel MUD scheme, Soft-PIC (soft parallel interference cancellation) is proposed. Based on the reliability of each detected user signal in the former stage, this Soft-PIC detection scheme substitutes a soft decision of the variable for the hard decision in PIC scheme. Compared with the PIC scheme, it can reconstruct the interference signals more accurately and eliminate MAI(multiple access interference) in a more efficient way.PIC is one of the most practical schemes in numerous multiuser detection technologies. However, Soft-PIC as an improved PIC scheme deserves further study.
基金Supported by National Natural Science Foundation of China(Nos.61201346,61175005 and 61401049)the Fundamental Research Funds for the Central Universities(No.CDJZR14125501)
文摘The 252Cf source-driven verification system(SDVS)can recognize the enrichment of fissile material with the enrichment-sensitive autocorrelation functions of a detector signal in252Cf source-driven noise-analysis(SDNA)measurements.We propose a parallel and optimized genetic Elman network(POGEN)to identify the enrichment of235U based on the physical properties of the measured autocorrelation functions.Theoretical analysis and experimental results indicate that,for 4 different enrichment fissile materials,due to higher information utilization,more efficient network architecture,and optimized parameters,the POGEN-based algorithm can obtain identification results with higher recognition accuracy,compared to the integrated autocorrelation function(IAF)method.
