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Raft Consensus Algorithm Based on Credit Model in Consortium Blockchain

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Wuhan University Journal of Natural Sciences 2020年第2期25卷 146-154页

作者： CHEN Yunfang LIU Ping ZHANG Wei School of Computer Science Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina Jiangsu Key laboratory of Big Data security and intelligent Processing Nanjing University of Posts and TelecommunicationsNanjing 210023JiangsuChina

As one of the underlying technologies of the blockchain,the consensus algorithm plays a vital role in ensuring security and *** a consensus algorithm for the private blockchain,Raft has better performance than the rest of the consensus algorithms,and it does not cause problems such as the concentrated hashing power,resource waste and ***,Raft can only be used in a non-byzantine environment with a small network *** order to enable Raft to be used in a large-scale network with a certain number of byzantine nodes,this paper combines Raft and credit model to propose a Raft blockchain consensus algorithm based on credit model *** the node credit evaluation phase,RBF-based support vector machine is used as the anomaly detection method,and the node credit evaluation model is *** the Trust Nodes List(TNL)mechanism is introduced to make the consensus phase in a creditable network ***,the common node is synchronized to the consensus node to update the blockchain of the entire *** show that CRaft has better throughput and lower latency than the commonly used consortium blockchain consensus algorithm PBFT(Practical Byzantine Fault Tolerance).

关键词： credit model consensus algorithm consortium blockchain P2P network

On the combination of data augmentation method and gated convolution model for building effective and robust intrusion detection

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Cybersecurity 2018年第1期1卷 933-944页

作者： Yixiang Wang Shaohua lv Jiqiang Liu Xiaolin Chang Jinqiang Wang Beijing Key laboratory of security and Privacy in intelligent Transportation Beijing Jiaotong University3Shangyuancun100044 BeijingChina Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University3 Shangyuancun100044 BeijingChina

Deep learning(DL)has exhibited its exceptional performance in fields like intrusion *** augmentation methods have been proposed to improve data quality and eventually to enhance the performance of DL ***,the classic augmentation methods cannot be applied to those DL models which exploit the system-call sequences to detect ***,the seq2seq model has been explored to augment system-call *** this work,we propose a gated convolutional neural network(GCNN)model to thoroughly extract the potential information of augmented ***,in order to enhance themodel’s robustness,we adopt adversarial training to reduce the impact of adversarial examples on the *** examples used in adversarial training are generated by the proposed adversarial sequence generation *** experimental results on different verified models show that GCNN model can better obtain the potential information of the augmented data and achieve the best ***,GCNN with adversarial training can enhance robustness significantly.

关键词： Data augmentation Intrusion detection system Machine learning algorithms System call

Robust Transmission Capacity Margin evaluation Based on All-Scenario-Feasible Optimization

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Robust Transmission Capacity Margin Evaluation Based on All-...

A Sensor-Service Collaboration Approach for Target Tracking in Wireless Camera networks

第36届中国控制会议

作者： Weisheng Liu Xiaohong Guan Jiang Wu Feilong Song Dong Xie Ministry of Education Key Lab for intelligent networks and network security Xi’an Jiaotong University

ISBN: (纸本)9781538629185

A robust and effective evaluation method for the safety of optimal generation scheduling has great potential on optimizing operation of electrical power and energy *** on the concept of transmission capacity margin,a robust model of transmission capacity margin evaluation（TCME） is established to assess the overload risk of transmission lines when loads *** TCME method can provide the system operators with useful information such as possibly overloaded lines and time periods when lines are potentially overloaded under the uncertainties of nodal ***,TCME formulation has a multi-level（min-max） structure and the solution methods are usually *** order to overcome the computational complexity of original problem,an All-Scenario-Feasible optimization is *** is established based on very limited scenarios while the solution is feasible to all *** proposed formulation is single-level rather than *** no iterative algorithms are needed and commercial solver like CPLEX could be directly *** order to use the proposed formulation for large-scale power grid,considering that the transmission capacity constraint is the most important one in all problem constraints,we further reduce the number of *** testing is also performed and the results show that the proposed formulation is effective.

关键词： robust transmission capacity margin All-Scenario-Feasible

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China Communications 2017年第7期14卷 44-56页

作者： Shuai Zhao Le Yu State Key laboratory of networking and Switching Technology Beijing University of Posts and Telecommunications China Mobile Information security Center

Mobile target tracking is a necessary function of some emerging application domains, such as virtual reality, smart home and intelligent healthcare. However, existing portable devices for target tracking are resource intensive and high-cost. Camera tracking is an effective location tracking way for those emerging applications which can reuse the existing ubiquitous video monitoring system. This paper proposes a dynamic community-based camera collaboration(D3C) framework for target location and tracking. The contributions of D3C mainly include that(1) nonlinear perspective projection model is selected as the camera sensing model and sequential Monte Carlo is employed to predict the target location;(2) a dynamic collaboration scheme is proposed, it is based on the local community-detection theory deriving from social network analysis. The performance of proposed approach is validated by both synthetic datasets and real-world application. The experiment results show that D3C meets the versatility, real-time and fault tolerance requirements of target tracking applications.

关键词： service collaboration camera tracking community detection sequential monte carlo

Intrusion detection Model with Twin Support Vector Machines

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Journal of Shanghai Jiaotong university(Science) 2014年第4期19卷 448-454页

作者：何俊郑世慧 Information security Center National Engineering laboratory for Disaster Backup and Recovery Beijing University of Posts and Telecommunications

Intrusion detection system(IDS) is becoming a critical component of network security. However,the performance of many proposed intelligent intrusion detection models is still not competent to be applied to real network security. This paper aims to explore a novel and effective approach to significantly improve the performance of IDS. An intrusion detection model with twin support vector machines(TWSVMs) is *** this model, an efficient algorithm is also proposed to determine the parameter of TWSVMs. The performance of the proposed intrusion detection model is evaluated with KDD'99 dataset and is compared with those of some recent intrusion detection models. The results demonstrate that the proposed intrusion detection model achieves remarkable improvement in intrusion detection rate and more balanced performance on each type of ***, TWSVMs consume much less training time than standard support vector machines(SVMs).

关键词： network security twin support vector machine(TWSVM) parameter determination

Bio-inspired Hybrid Feature Selection Model for Intrusion detection

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Computers, Materials & Continua 2022年第10期73卷 133-150页

作者： Adel Hamdan Mohammad Tariq Alwada’n Omar Almomani Sami Smadi Nidhal ElOmari Computer Science Department The World Islamic Sciences and Education UniversityAmmanJordan network and Cybersecurity Department Teesside UniversityMiddlesbroughUnited Kingdom Information and network security The World Islamic Sciences and Education UniversityAmmanJordan Software Engineering The World Islamic Sciences and Education UniversityAmmanJordan

Intrusion detection is a serious and complex *** due to a large number of attacks around the world,the concept of intrusion detection has become very *** research proposes a multilayer bioinspired feature selection model for intrusion detection using an optimized genetic ***,the proposed multilayer model consists of two layers(layers 1 and 2).At layer 1,three algorithms are used for the feature *** algorithms used are Particle Swarm Optimization(PSO),Grey Wolf Optimization(GWO),and Firefly Optimization Algorithm(FFA).At the end of layer 1,a priority value will be assigned for each feature *** layer 2 of the proposed model,the Optimized Genetic Algorithm(GA)is used to select one feature set based on the priority *** are done on standard GA to perform optimization and to fit the proposed *** Optimized GA is used in the training phase to assign a priority value for each feature ***,the priority values are categorized into three categories:high,medium,and ***,the Optimized GA is used in the testing phase to select a feature set based on its *** feature set with a high priority will be given a high priority to be *** the end of phase 2,an update for feature set priority may occur based on the selected features priority and the calculated *** proposed model can learn and modify feature sets priority,which will be reflected in selecting *** evaluation purposes,two well-known datasets are used in these *** first dataset is UNSW-NB15,the other dataset is the *** evaluation criteria are used,such as precision,recall,and *** experiments in this research suggest that the proposed model has a powerful and promising mechanism for the intrusion detection system.

关键词： Intrusion detection Machine learning Optimized Genetic Algorithm(GA) Particle Swarm Optimization algorithms(PSO) Grey Wolf Optimization algorithms(GWO) FireFly Optimization Algorithms(FFA) Genetic Algorithm(GA)

An Early Warning Model of Telecommunication network Fraud Based on User Portrait

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计算机、材料和连续体（英文） 2023年第4期75卷 1561-1576页

作者： Wen Deng Guangjun Liang Chenfei Yu Kefan Yao Chengrui Wang Xuan Zhang Department of Computer Information and Cyber security Jiangsu Police InstituteNanjingChina Engineering Research Center of Electronic Data Forensics Analysis NanjingChina Department of Public security of Jiangsu Province Key Laboratory of Digital ForensicsNanjingChina

With the frequent occurrence of telecommunications and networkfraud crimes in recent years, new frauds have emerged one after another whichhas caused huge losses to the people. However, due to the lack of an effectivepreventive mechanism, the police are often in a passive position. Usingtechnologies such as web crawlers, feature engineering, deep learning, andartificial intelligence, this paper proposes a user portrait fraudwarning schemebased on Weibo public data. First, we perform preliminary screening andcleaning based on the keyword “defrauded” to obtain valid fraudulent userIdentity Documents (IDs). The basic information and account information ofthese users is user-labeled to achieve the purpose of distinguishing the typesof fraud. Secondly, through feature engineering technologies such as avatarrecognition, Artificial Intelligence (AI) sentiment analysis, data screening,and follower blogger type analysis, these pictures and texts will be abstractedinto user preferences and personality characteristics which integrate multidimensionalinformation to build user portraits. Third, deep neural networktraining is performed on the cube. 80% percent of the data is predicted basedon the N-way K-shot problem and used to train the model, and the remaining20% is used for model accuracy evaluation. Experiments have shown thatFew-short learning has higher accuracy compared with Long Short TermMemory (LSTM), Recurrent Neural networks (RNN) and ConvolutionalNeural network (CNN). On this basis, this paper develops a WeChat smallprogram for early warning of telecommunications network fraud based onuser portraits. When the user enters some personal information on the frontend, the back-end database can perform correlation analysis by itself, so as tomatch the most likely fraud types and give relevant early warning *** fraud warning model is highly scaleable. The data of other Applications(APPs) can be extended to further improve the efficiency of anti-fraud whichhas e

关键词： Crawler user portrait feature engineering deep learning small program development

The detection of Fraudulent Smart Contracts Based on ECA-EfficientNet and Data Enhancement

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Computers, Materials & Continua 2023年第12期77卷 4073-4087页

作者： Xuanchen Zhou Wenzhong Yang Liejun Wang Fuyuan Wei KeZiErBieKe HaiLaTi Yuanyuan Liao College of Software Xinjiang UniversityUrumqi830000China Key laboratory of Signal detection and Processing in Xinjiang Uygur Autonomous Region Urumqi830000China Key laboratory of Multilingual Information Technology in Xinjiang Uygur Autonomous Region Urumqi830000China

With the increasing popularity of Ethereum,smart contracts have become a prime target for fraudulent activities such as Ponzi,honeypot,gambling,and phishing *** some researchers have studied intelligent fraud detection,most research has focused on identifying Ponzi contracts,with little attention given to detecting and preventing gambling or phishing *** are three main issues with current ***,there exists a severe data imbalance between fraudulent and non-fraudulent ***,the existing detection methods rely on diverse raw features that may not generalize well in identifying various classes of fraudulent ***,most prior studies have used contract source code as raw features,but many smart contracts only exist in *** address these issues,we propose a fraud detection method that utilizes Efficient Channel Attention EfficientNet(ECA-EfficientNet)and data *** method begins by converting bytecode into Red Green Blue(RGB)three-channel images and then applying channel exchange data *** then use the enhanced ECA-EfficientNet approach to classify fraudulent smart contract RGB *** proposed method achieves high F1-score and Recall on both publicly available Ponzi datasets and self-built multi-classification datasets that include Ponzi,honeypot,gambling,and phishing smart *** results of the experiments demonstrate that our model outperforms current methods and their variants in Ponzi contract *** research addresses a significant problem in smart contract security and offers an effective and efficient solution for detecting fraudulent contracts.

关键词： Fraud detection smart contract ECA-EfficientNet Ethereum

SSD Real-Time Illegal Parking detection Based on Contextual Information Transmission

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Computers, Materials & Continua 2020年第1期62卷 293-307页

作者： Huanrong Tang Aoming Peng Dongming Zhang Tianming Liu Jianquan Ouyang Key laboratory of intelligent Computing and Information Processing Ministry of EducationCollege of Information EngineeringXiangtan UniversityXiangtan411105China National Computer network Emergency Response Technical Team/Coordination Center of China Beijing100029China Department of Computer Science the University of GeorgiaAthensGeorgiaUSA

With the improvement of the national economic level,the number of vehicles is still increasing year by *** to the statistics of National Bureau of Statics,the number is approximately up to 327 million in China by the end of 2018,which makes urban traffic pressure continues to rise so that the negative impact of urban traffic order is *** parking-the common problem in the field of transportation security is urgent to be solved and traditional methods to address it are mainly based on ground loop and manual supervision,which may miss detection and cost much *** to the rapidly developing deep learning sweeping the world in recent years,object detection methods relying on background segmentation cannot meet the requirements of complex and various scenes on speed and ***,an improved Single Shot MultiBox Detector(SSD)based on deep learning is proposed in our study,we introduce attention mechanism by spatial transformer module which gives neural networks the ability to actively spatially transform feature maps and add contextual information transmission in specified ***,we found out the best connection layer in the detection model by repeated experiments especially for small objects and increased the precision by 1.5%than the baseline SSD without extra training ***,we designed an illegal parking vehicle detection method by the improved SSD,reaching a high precision up to 97.3%and achieving a speed of 40FPS,superior to most of vehicle detection methods,will make contributions to relieving the negative impact of illegal parking.

关键词： Contextual information transmission illegal parking detection spatial attention mechanism deep learning