|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | An Abnormal Network Flow Feature Sequence Prediction Approach for DDoS Attacks Detection in Big Data Environment显示文摘Distributed denial-of-service(DDoS)is a rapidly growing problem with the fast development of the Internet.There are multitude DDoS detection approaches,however,three major problems about DDoS attack detection appear in the big data environment.Firstly,to shorten the respond time of the DDoS attack detector;secondly,to reduce the required compute resources;lastly,to achieve a high detection rate with low false alarm rate.In the paper,we propose an abnormal network flow feature sequence prediction approach which could fit to be used as a DDoS attack detector in the big data environment and solve aforementioned problems.We define a network flow abnormal index as PDRA with the percentage of old IP addresses,the increment of the new IP addresses,the ratio of new IP addresses to the old IP addresses and average accessing rate of each new IP address.We design an IP address database using sequential storage model which has a constant time complexity.The autoregressive integrated moving average(ARIMA)trending prediction module will be started if and only if the number of continuous PDRA sequence value,which all exceed an PDRA abnormal threshold(PAT),reaches a certain preset threshold.And then calculate the probability that is the percentage of forecasting PDRA sequence value which exceed the PAT.Finally we identify the DDoS attack based on the abnormal probability of the forecasting PDRA sequence.Both theorem and experiment show that the method we proposed can effectively reduce the compute resources consumption,identify DDoS attack at its initial stage with higher detection rate and lower false alarm rate. | Jieren Cheng Ruomeng Xu Xiangyan Tang Victor S.Sheng Canting Cai | 2018 | Computers, Materials & Continua2018，，4： | 15 |
| 2 | An Improved Method for the Fitting and Prediction of the Number of COVID-19 Confirmed Cases Based on LSTM显示文摘New coronavirus disease(COVID-19)has constituted a global pandemic and has spread to most countries and regions in the world.Through understanding the development trend of confirmed cases in a region,the government can control the pandemic by using the corresponding policies.However,the common traditional mathematical differential equations and population prediction models have limitations for time series population prediction,and even have large estimation errors.To address this issue,we propose an improved method for predicting confirmed cases based on LSTM(Long-Short Term Memory)neural network.This work compares the deviation between the experimental results of the improved LSTM prediction model and the digital prediction models(such as Logistic and Hill equations)with the real data as reference.Furthermore,this work uses the goodness of fitting to evaluate the fitting effect of the improvement.Experiments show that the proposed approach has a smaller prediction deviation and a better fitting effect.Compared with the previous forecasting methods,the contributions of our proposed improvement methods are mainly in the following aspects:1)we have fully considered the spatiotemporal characteristics of the data,rather than single standardized data.2)the improved parameter settings and evaluation indicators are more accurate for fitting and forecasting.3)we consider the impact of the epidemic stage and conduct reasonable data processing for different stage. | Bingjie Yan Jun Wang Zhen Zhang Xiangyan Tang Yize Zhou Guopeng Zheng Qi Zou Yao Lu Boyi Liu Wenxuan Tu Neal Xiong | 2020 | Computers, Materials & Continua2020，，9： | 2 |
| 3 | Blockchain Security Threats and Collaborative Defense:A Literature Review显示文摘As a distributed database,the system security of the blockchain is of great significance to prevent tampering,protect privacy,prevent double spending,and improve credibility.Due to the decentralized and trustless nature of blockchain,the security defense of the blockchain system has become one of the most important measures.This paper comprehensively reviews the research progress of blockchain security threats and collaborative defense,and we first introduce the overview,classification,and threat assessment process of blockchain security threats.Then,we investigate the research status of single-node defense technology and multi-node collaborative defense technology and summarize the blockchain security evaluation indicators and evaluation methods.Finally,we discuss the challenges of blockchain security and future research directions,such as parallel detection and federated learning.This paper aims to stimulate further research and discussion on blockchain security,providing more reliable security guarantees for the use and development of blockchain technology to face changing threats and challenges through continuous updating and improvement of defense technologies. | Xiulai Li Jieren Cheng Zhaoxin Shi Jingxin Liu Bin Zhang Xinbing Xu Xiangyan Tang Victor S.Sheng | 2023 | Computers, Materials & Continua2023，76，9： | 1 |
| 4 | Novel DDoS Feature Representation Model Combining Deep Belief Network and Canonical Correlation Analysis显示文摘Distributed denial of service(DDoS)attacks launch more and more frequently and are more destructive.Feature representation as an important part of DDoS defense technology directly affects the efficiency of defense.Most DDoS feature extraction methods cannot fully utilize the information of the original data,resulting in the extracted features losing useful features.In this paper,a DDoS feature representation method based on deep belief network(DBN)is proposed.We quantify the original data by the size of the network flows,the distribution of IP addresses and ports,and the diversity of packet sizes of different protocols and train the DBN in an unsupervised manner by these quantified values.Two feedforward neural networks(FFNN)are initialized by the trained deep belief network,and one of the feedforward neural networks continues to be trained in a supervised manner.The canonical correlation analysis(CCA)method is used to fuse the features extracted by two feedforward neural networks per layer.Experiments show that compared with other methods,the proposed method can extract better features. | Chen Zhang Jieren Cheng Xiangyan Tang Victor SSheng Zhe Dong Junqi Li | 2019 | Computers, Materials & Continua2019，，8： | 1 |
| 5 | A Modified PointNet-Based DDoS Attack Classification and Segmentation in Blockchain显示文摘With the rapid development of blockchain technology,the number of distributed applications continues to increase,so ensuring the security of the network has become particularly important.However,due to its decentralized,decentralized nature,blockchain networks are vulnerable to distributed denial-of-service(DDoS)attacks,which can lead to service stops,causing serious economic losses and social impacts.The research questions in this paper mainly include two aspects:first,the classification of DDoS,which refers to detecting whether blockchain nodes are suffering DDoS attacks,that is,detecting the data of nodes in parallel;The second is the problem of DDoS segmentation,that is,multiple pieces of data that appear at the same time are determined which type of DDoS attack they belong to.In order to solve these problems,this paper proposes a modified PointNet(MPointNet)for the classification and type segmentation of DDoS attacks.A dataset containing multiple DDoS attack types was constructed using the CIC-DDoS2019 dataset,and trained,validated,and tested accordingly.The results show that the proposed DDoS attack classification method has high performance and can be used for the actual blockchain security maintenance process.The accuracy rate of classification tasks reached 99.65%,and the accuracy of type segmentation tasks reached 85.47%.Therefore,the method proposed in this paper has high application value in detecting the classification and segmentation of DDoS attacks. | Jieren Cheng Xiulai Li Xinbing Xu Xiangyan Tang Victor S.Sheng | 2023 | Computer Systems Science & Engineering2023，47，10： | 1 |
| 6 | Forecasting Model Based on Information-Granulated GA-SVR and ARIMA for Producer Price Index显示文摘The accuracy of predicting the Producer Price Index(PPI)plays an indispensable role in government economic work.However,it is difficult to forecast the PPI.In our research,we first propose an unprecedented hybrid model based on fuzzy information granulation that integrates the GA-SVR and ARIMA(Autoregressive Integrated Moving Average Model)models.The fuzzy-information-granulation-based GA-SVR-ARIMA hybrid model is intended to deal with the problem of imprecision in PPI estimation.The proposed model adopts the fuzzy information-granulation algorithm to pre-classification-process monthly training samples of the PPI,and produced three different sequences of fuzzy information granules,whose Support Vector Regression(SVR)machine forecast models were separately established for their Genetic Algorithm(GA)optimization parameters.Finally,the residual errors of the GA-SVR model were rectified through ARIMA modeling,and the PPI estimate was reached.Research shows that the PPI value predicted by this hybrid model is more accurate than that predicted by other models,including ARIMA,GRNN,and GA-SVR,following several comparative experiments.Research also indicates the precision and validation of the PPI prediction of the hybrid model and demonstrates that the model has consistent ability to leverage the forecasting advantage of GA-SVR in non-linear space and of ARIMA in linear space. | Xiangyan Tang Liang Wang Jieren Cheng Jing Chen Victor S.Sheng | 2019 | Computers, Materials & Continua2019，，2： | 1 |
| 7 | An Improved BPNN Prediction Method Based on Multi-Strategy Sparrow Search Algorithm显示文摘Data prediction can improve the science of decision-making by making predictions about what happens in daily life based on natural law trends.Back propagation(BP)neural network is a widely used prediction method.To reduce its probability of falling into local optimum and improve the prediction accuracy,we propose an improved BP neural network prediction method based on a multi-strategy sparrow search algorithm(MSSA).The weights and thresholds of the BP neural network are optimized using the sparrow search algorithm(SSA).Three strategies are designed to improve the SSA to enhance its optimization-seeking ability,leading to the MSSA-BP prediction model.The MSSA algorithm was tested with nine different types of benchmark functions to verify the optimization performance of the algorithm.Two different datasets were selected for comparison experiments on three groups of models.Under the same conditions,the mean absolute error(MAE),root mean square error(RMSE),andmean absolute percentage error(MAPE)of the prediction results of MSSA-BPwere significantly reduced,and the convergence speed was significantly improved.MSSA-BP can effectively improve the prediction accuracy and has certain application value. | Xiangyan Tang Dengfang Feng KeQiu Li Jingxin Liu Jinyang Song Victor S.Sheng | 2023 | Computers, Materials & Continua2023，，2： | 0 |
| 8 | DDoS Attack Detection via Multi-Scale Convolutional Neural Network显示文摘Distributed Denial-of-Service(DDoS)has caused great damage to the network in the big data environment.Existing methods are characterized by low computational efficiency,high false alarm rate and high false alarm rate.In this paper,we propose a DDoS attack detection method based on network flow grayscale matrix feature via multi-scale convolutional neural network(CNN).According to the different characteristics of the attack flow and the normal flow in the IP protocol,the seven-tuple is defined to describe the network flow characteristics and converted into a grayscale feature by binary.Based on the network flow grayscale matrix feature(GMF),the convolution kernel of different spatial scales is used to improve the accuracy of feature segmentation,global features and local features of the network flow are extracted.A DDoS attack classifier based on multi-scale convolution neural network is constructed.Experiments show that compared with correlation methods,this method can improve the robustness of the classifier,reduce the false alarm rate and the missing alarm rate. | Jieren Cheng Yifu Liu Xiangyan Tang Victor SSheng Mengyang Li Junqi Li | 2020 | Computers, Materials & Continua2020，，3： | 0 |
| 9 | A Novel DDoS Attack Detection Method Using Optimized Generalized Multiple Kernel Learning显示文摘Distributed Denial of Service(DDoS)attack has become one of the most destructive network attacks which can pose a mortal threat to Internet security.Existing detection methods cannot effectively detect early attacks.In this paper,we propose a detection method of DDoS attacks based on generalized multiple kernel learning(GMKL)combining with the constructed parameter R.The super-fusion feature value(SFV)and comprehensive degree of feature(CDF)are defined to describe the characteristic of attack flow and normal flow.A method for calculating R based on SFV and CDF is proposed to select the combination of kernel function and regularization paradigm.A DDoS attack detection classifier is generated by using the trained GMKL model with R parameter.The experimental results show that kernel function and regularization parameter selection method based on R parameter reduce the randomness of parameter selection and the error of model detection,and the proposed method can effectively detect DDoS attacks in complex environments with higher detection rate and lower error rate. | Jieren Cheng Junqi Li Xiangyan Tang Victor SSheng Chen Zhang Mengyang Li | 2020 | Computers, Materials & Continua2020，，3： | 0 |
| 10 | Ccdc57 is required for straightening the body axis by regulating ciliary motility in the brain ventricle of zebrafish显示文摘Recently,cilia defects have been proposed to contribute to scoliosis.Here,we demonstrate that coiled-coil domain-containing 57(Ccdc57)plays an essential role in straightening the body axis of zebrafish by regulating ciliary beating in the brain ventricle(BV).Zygotic ccdc57(Zccdc57)mutant zebrafish developes scoliosis without significant changes in their bone density and calcification,and the maternal-zygotic ccdc57(MZccdc57)mutant embryos display curved bodies since the long-pec stage.The expression of ccdc57 is enriched in ciliated tissues and immunofluorescence analysis reveals colocalization of Ccdc57-HA with acetylated a-tubulin,implicating it in having a role in ciliary function.Further examination reveals that it is the coordinated cilia beating of multiple cilia bundles(MCB)in the MZccdc57 mutant embryos that is affected at 48 hours post fertilization,when the compromised cerebrospinal fluid flow and curved body axis have already occurred.Either ccdc57 m RNA injection or epinephrine treatment reverses the spinal curvature in MZccdc57 mutant larvae from ventrally curly to straight or even dorsally curly and significantly upregulates urotensin signaling.This study reveals the role of ccdc57 in maintaining coordinated cilia beating of MCB in the BV. | Lu Li Juan Li Yuan Ou Jiaxin Wu Huilin Li Xin Wang Liying Tang Xiangyan Dai Conghui Yang Zehong Wei Zhan Yin Yuqin Shu | 2023 | Journal of Genetics and Genomics2023，50，4： | 0 |
| 11 | PoEC: A Cross-Blockchain Consensus Mechanism for Governing Blockchain by Blockchain显示文摘The research on the governing blockchain by blockchain supervision system is an important development trend of blockchain technology.In this system there is a supervisory blockchain managing and governing the supervised blockchain based on blockchain technology,results in a uniquely cross-blockchain demand to consensus mechanism for solving the trust problem between supervisory blockchain and supervised blockchain.To solve this problem,this paper proposes a cross-blockchain consensus mechanism based on smart contract and a set of smart contracts endorse the crossblockchain consensus.New consensus mechanism called Proof-of-EndorseContracts(PoEC)consensus,which firstly transfers the consensus reached in supervisory blockchain to supervised blockchain by supervisory nodes,then packages the supervisory block in supervisory blockchain and transmits it to the smart contract deployed in the supervised blockchain,finally miners in supervised blockchain will execute and package the new block according to the status of the smart contract.The core part of the consensus mechanism is Endorse Contracts which designed and implemented by us and verified the effectiveness through experiments.PoEC consensus mechanism and Endorse Contracts support the supervised blockchain to join the governing blockchain by blockchain system without changing the original consensus mechanism,which has the advantages of low cost,high scalability and being able to crossblockchain.This paper proves that our method can provide a feasible crossblockchain governance scheme for the field of blockchain governance. | Jieren Cheng Yuan Zhang Yuming Yuan Hui Li Xiangyan Tang Victor S.Sheng Guangjing Hu | 2022 | Computers, Materials & Continua2022，，10： | 0 |
| 12 | An Adaptive DDoS Detection and Classification Method in Blockchain Using an Integrated Multi-Models显示文摘With the rising adoption of blockchain technology due to its decentralized,secure,and transparent features,ensuring its resilience against network threats,especially Distributed Denial of Service(DDoS)attacks,is crucial.This research addresses the vulnerability of blockchain systems to DDoS assaults,which undermine their core decentralized characteristics,posing threats to their security and reliability.We have devised a novel adaptive integration technique for the detection and identification of varied DDoS attacks.To ensure the robustness and validity of our approach,a dataset amalgamating multiple DDoS attacks was derived from the CIC-DDoS2019 dataset.Using this,our methodology was applied to detect DDoS threats and further classify them into seven unique attack subcategories.To cope with the broad spectrum of DDoS attack variations,a holistic framework has been pro-posed that seamlessly integrates five machine learning models:Gate Recurrent Unit(GRU),Convolutional Neural Networks(CNN),Long-Short Term Memory(LSTM),Deep Neural Networks(DNN),and Support Vector Machine(SVM).The innovative aspect of our framework is the introduction of a dynamic weight adjustment mechanism,enhancing the system’s adaptability.Experimental results substantiate the superiority of our ensemble method in comparison to singular models across various evaluation metrics.The framework displayed remarkable accuracy,with rates reaching 99.71%for detection and 87.62%for classification tasks.By developing a comprehensive and adaptive methodology,this study paves the way for strengthening the defense mechanisms of blockchain systems against DDoS attacks.The ensemble approach,combined with the dynamic weight adjustment,offers promise in ensuring blockchain’s enduring security and trustworthiness. | Xiulai Li Jieren Cheng Chengchun Ruan Bin Zhang Xiangyan Tang Mengzhe Sun | 2023 | Computers, Materials & Continua2023，77，12： | 0 |
| 13 | Study on the Crustal Structure beneath Seismic Station Bachu and Taxkorgan in Xinjiang显示文摘Using the teleseismic waveform data recorded by the seismic station Bachu( hereafter referred to as station BCH) i n the Tarim Basin and the seismic station Taxkorgan(h ereafter referred to as station TAG) i n the west Kunlun Mountains for years,we applied the receiver function H-κ stacking method to study the crustal structure beneath stations BCH and TAG. The results showed that there are obvious differences in the crustal thickness beneath stations BCH and TAG,and the regional crustal thickness and terrain have a very good corresponding relationship. There are high crustal average V P/ V S values beneath the two stations. The crustal thickness is 44 km,and the crustal average wave velocity ratio is 1. 849 beneath station BCH. There is a sharp discontinuity in the middle of the crust beneath station BCH at a depth of 21 km. There is a low average P wave velocity and low V P/ V S from the surface to the discontinuity beneath station BCH.The depth of the discontinuity is consistent with the lower interface of the focal depth from accurate location in the Jiashi earthquake source area adjacent to station BCH; and may be the crustal brittle-ductile conversion boundary. The crustal thickness is 69 km,and the crustal average wave velocity ratio is 1. 847 beneath station TAG,a thicker crust and high V P/ V S may indicate that materials in the lower crustal are prone to plastic flow,which is responsible for the thickening of the crust. | Tang Mingshuai Wang Haitao Luo Ju Kong Xiangyan Yang Feng | 2015 | Earthquake Research in China2015，29，2： | 0 |
| 14 | Machine Learning for Improving Stellar Image-based Alignment in Wide-field Telescopes显示文摘Stellar images will deteriorate dramatically when the sensitive elements of wide-field survey telescopes are misaligned during an observation, and active alignment is the key technology to maintain the high resolution of wide-field sky survey telescopes. Instead of traditional active alignment based on field-dependent wave front errors, this work proposes a machine learning alignment metrology based on stellar images of the scientific camera,which is more convenient and higher speed. We first theoretically confirm that the pattern of the point-spread function over the field is closely related to the misalignment status, and then the relationships are learned by twostep neural networks. After two-step active alignment, the position errors of misalignment parameters are less than5 μm for decenter and less than 5″ for tip-tilt in more than 90% of the cases. The precise alignment results indicate that this metrology provides a low-cost and high-speed solution to maintain the image quality of wide-field sky survey telescopes during observation, thus implying important significance and broad application prospects. | Zhixu Wu Yiming Zhang Rongxin Tang Zhengyang Li Xiangyan Yuan Yong Xia Hua Bai Bo Li Zhou Chen Xiangqun Cui Xiaohua Deng | 2022 | Research in Astronomy and Astrophysics2022，22，1： | 0 |
| 15 | A DDoS Attack Information Fusion Method Based on CNN for Multi-Element Data显示文摘Traditional distributed denial of service(DDoS)detection methods need a lot of computing resource,and many of them which are based on single element have high missing rate and false alarm rate.In order to solve the problems,this paper proposes a DDoS attack information fusion method based on CNN for multi-element data.Firstly,according to the distribution,concentration and high traffic abruptness of DDoS attacks,this paper defines six features which are respectively obtained from the elements of source IP address,destination IP address,source port,destination port,packet size and the number of IP packets.Then,we propose feature weight calculation algorithm based on principal component analysis to measure the importance of different features in different network environment.The algorithm of weighted multi-element feature fusion proposed in this paper is used to fuse different features,and obtain multi-element fusion feature(MEFF)value.Finally,the DDoS attack information fusion classification model is established by using convolutional neural network and support vector machine respectively based on the MEFF time series.Experimental results show that the information fusion method proposed can effectively fuse multi-element data,reduce the missing rate and total error rate,memory resource consumption,running time,and improve the detection rate. | Jieren Cheng Canting Cai Xiangyan Tang Victor SSheng Wei Guo Mengyang Li | 2020 | Computers, Materials & Continua2020，，4： | 0 |
| 16 | Pain-related mediators underlie incision-induced mechanical nociception in the dorsal root ganglia显示文摘Approximately 50–70%of patients experience incision-induced mechanical nociception after surgery.However,the mechanism underlying incision-induced mechanical nociception is still unclear.Interleukin-10 and brain-derived neurotrophic factor are important pain mediators,but whether interleukin-10 and brain-derived neurotrophic factor are involved in incision-induced mechanical nociception remains uncertain.In this study,forty rats were divided randomly into the incision surgery(n=32)and sham surgery(n=8)groups.Plantar incision on the central part of left hind paw was performed under anesthesia in rats from the surgery group.Rats in the sham surgery group received anesthesia,but not an incision.Von Frey test results showed that,compared with the sham surgery group,incision surgery decreased the withdrawal threshold of rats at 0.5,3,6 and 24 hours after incision.Immunofluorescence staining in the dorsal root ganglia of the spinal cord(L3–5)showed that interleukin-10 and brain-derived neurotrophic factor were expressed mainly on smalland medium-sized neurons(diameter<20μm and 20–40μm)and satellite cells in the dorsal root ganglia of the spinal cord(L3–5)in the sham surgery group.By contrast,in the surgery group,high expression of interleukin-10 and brain-derived neurotrophic factor appeared in large-sized neurons(diameter>40μm)at 6 and 24 hours after incision surgery,which corresponded to the decreased mechanical withdrawal threshold of rats in the surgery group.These experimental findings suggest that expression pattern shift of interleukin-10 and brain-derived neurotrophic factor induced by incision surgery in dorsal root ganglia of rats was closely involved in lowering the threshold to mechanical stimulus in the hind paw following incision surgery.Pain-related mediators induced by incision surgery in dorsal root ganglia of rats possibly underlie mechanical nociception in ipsilateral hind paws. | Xiuhong Yuan Xiangyan Liu Qiuping Tang Yunlong Deng | 2013 | Neural Regeneration Research2013，8，35： | 0 |
| 17 | Global multifaceted biodiversity patterns,centers,and conservation needs in angiosperms显示文摘The Convention on Biological Diversity seeks to conserve at least 30%of global land and water areas by 2030,which is a challenge but also an opportunity to better preserve biodiversity,including flowering plants(angiosperms).Herein,we compiled a large database on distributions of over 300,000 angiosperm species and the key functional traits of 67,024 species.Using this database,we constructed biodiversity-environment models to predict global patterns of taxonomic,phylogenetic,and functional diversity in terrestrial angiosperms and provide a comprehensive mapping of the three diversity facets.We further evaluated the current protection status of the biodiversity centers of these diversity facets.Our results showed that geographical patterns of the three facets of plant diversity exhibited substantial spatial mismatches and nonoverlapping conservation priorities.Idiosyncratic centers of functional diversity,particularly of herbaceous species,were primarily distributed in temperate regions and under weaker protection compared with other biodiversity centers of taxonomic and phylogenetic facets.Our global assessment of multifaceted biodiversity patterns and centers highlights the insufficiency and unbalanced conservation among the three diversity facets and the two growth forms(woody vs.herbaceous),thus providing directions for guiding the future conservation of global plant diversity. | Ao Luo Yaoqi Li Nawal Shrestha Xiaoting Xu Xiangyan Su Yichao Li Tong Lyu Kilara Waris Zhiyao Tang Xiaojuan Liu Luxiang Lin Yongsheng Chen Kuiling Zu Wenqi Song Shijia Peng Niklaus E.Zimmermann Loïc Pellissier Zhiheng Wang | 2024 | Science China(Life Sciences)2024，67，4： | 0 |
您所在的机构暂未开通此部分的功能，请联系管理员