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Identifying heavy hitters in high-speed network monitoring

Science china(Information Sciences) 2010年第3期53卷 659-676页

作者： zhang yu1, fang binxing1,2.& {3. yongzheng2.1research center of computer network and Information Security technology, harbin institute of technology, harbin 150001, china 2.12} center of Information Security, institute of Computing technology, Chinese Academy of Sciences, Beijing 100190, china research center of {2. {3. and Information Security technology Harbin Institute of Technology Harbin China research center of Information Security Institute of Computing Technology Chinese Academy of Sciences Beijing China

Identifying heavy hitters in a network traffic stream is important for a variety of network applications ranging from traffic engineering to anomaly detection such as detection of denial-of-service attacks. Existing methods generally examine newly arriving items in the stream, perform a small number of operations using a small amount of memory, and still provide guarantees on the identifying accuracy. In high-speed network monitoring, the update speed per item is extremely critical. However, so far as we know, there are no identifying algorithms which can provide constant update time (O(1)) in a weighted data stream. In this paper, we present an algorithm named Weighted Lossy Counting (WLC) which is able to identify heavy hitters in a high-speed weighted data stream with constant update time. WLC employs a novel efficient partially ordered data structure which is able to provide a fast per-item update speed while keeping the memory cost relatively low. We compare WLC with state-of-the-art algorithms for finding heavy hitters in real traffic traces. The experimental results show that WLC performs well in accuracy (recall, precision and average relative error) as other algorithms; moreover it has a much higher update speed at the cost of relatively larger memory space used. A theoretical worst-case memory bound of WLC is also derived in this paper; however, experiments with long real traffic traces show that WLC requires much less space than the theoretical bound in practice.

关键词： network traffic monitoring heavy hitter weighted data streams

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基于联盟式区块链的域名系统根区管理体系

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信息安全研究 2024年第7期10卷 602-615页

作者：张宇冯禹铭张伟哲方滨兴哈尔滨工业大学网络空间安全学院哈尔滨150001 广州大学网络空间先进技术研究院广州510006 鹏城实验室新型网络研究部广东深圳518055 哈尔滨工业大学(深圳)计算机科学与技术学院广东深圳518055

当前互联网域名系统的中心化根体系伴随着长期的担忧:一方面担忧国家代码顶级域可能由于根权威职能被破坏而失控;另一方面担忧去中心化的根替代方案会导致域名空间分裂.上述担忧的根源在于当前和替代的根区管理在自治化和透明化上不足,... 详细信息

当前互联网域名系统的中心化根体系伴随着长期的担忧:一方面担忧国家代码顶级域可能由于根权威职能被破坏而失控;另一方面担忧去中心化的根替代方案会导致域名空间分裂.上述担忧的根源在于当前和替代的根区管理在自治化和透明化上不足,导致对当前的根权威或替代方案的不信任.为解决上述问题,提出一种新的域名系统根区管理体系——根共识链,通过增强互信缓解各方担忧.根共识链中多个自治的注册局共同参与根区管理,每个注册局下辖国家代码顶级域和根服务器运营者,共同构建一个基于联盟式区块链的根区管理体系.根共识链在维护统一域名空间和唯一全球根权威的同时,通过根共识链管理者们建立根共同体提高自治性,通过区块链记录和执行各方协议以及根区操作提高透明性.基于现网科研测试床的实验结果表明,根共识链能够有效应对上述担忧,具有良好的可行性与实用性.

关键词：域名系统根服务器根区管理联盟式区块链注册局

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Ripple+:An Improved Scheme of Ripple Consensus Protocol in Deployability,Liveness and Timing Assumption

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computer Modeling in Engineering & Sciences 2022年第1期130卷 463-481页

作者： Chuanwang Ma yu zhang {2. {3. Hongli zhang Yidong Jin Dasheng Zhou School of Cyberspace Science Harbin Institute of TechnologyHarbin150001China Peng Cheng Laboratory Shenzhen518055China Beijing Jingning Data technology Co. Ltd.Beijing100007China

Ripple acts as a real-time settlement and payment system to connect banks and payment *** the consensus support of the Ripple network to ensure network consistency,Ripple consensus protocol has been widely concerned in recent *** with those Byzantine fault tolerant protocols,Ripple has a significant difference that the system can reach an agreement under decentralized trust ***,Ripple has many problems both in theory and practice,which arementioned in the previous *** paper presents Ripple+,an improved scheme of Ripple consensus protocol,which improves Ripple fromthree aspects:(1)Ripple+employs a specific trustmodel and a corresponding guideline for Unique Node List selection,which makes it easy to deploy in practice to meet the safety and liveness condition;(2.the primary and viewchangemechanismare joined to solve the problem discussed by the previous {2. that Ripple may lose liveness in some extreme scenarios;(3.we remove the strong synchrony clock and timeout during consensus periods to make it suitable for weak synchrony *** implemented a prototype of Ripple+and conducted experiments to show that Ripple+can achieve the throughput of tens of thousands of transactions per second with no more than half a minute latency,and the view change mechanism hardly incurs additional cost.

关键词： Ripple consensus decentralized trust byzantine fault tolerant protocol

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Prison Term Prediction on Criminal Case Description with Deep Learning

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computers, Materials & Continua 2020年第3期62卷 1217-1231页

作者： Shang Li Hongli zhang Lin Ye Shen Su Xiaoding Guo Haining yu {2. {3. School of computer Science and technology Harbin Institute of TechnologyHarbinChina Cyberspace institute of Advanced technology Guangzhou UniversityGuangzhouChina Department of computer Science City University of Hong KongKowloon TongHong Kong

The task of prison term prediction is to predict the term of penalty based on textual fact description for a certain type of criminal *** advances in deep learning frameworks inspire us to propose a two-step method to address this *** obtain a better understanding and more specific representation of the legal texts,we summarize a judgment model according to relevant law articles and then apply it in the extraction of case feature from judgment *** formalizing prison term prediction as a regression problem,we adopt the linear regression model and the neural network model to train the prison term *** experiments,we construct a real-world dataset of theft case judgment *** results demonstrate that our method can effectively extract judgment-specific case features from textual fact *** best performance of the proposed predictor is obtained with a mean absolute error of 3.2.87 months,and the accuracy of 72.54%and 90.01%at the error upper bounds of three and six months,respectively.

关键词： Neural networks prison term prediction criminal case text comprehension

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Predictive modeling of 3.-day readmission risk of diabetes patients by logistic regression,artificial neural network,and EasyEnsemble

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Asian Pacific Journal of Tropical Medicine 2021年第9期14卷 417-428页

作者： Xiayu Xiang Chuanyi Liu Yanchun zhang Wei Xiang {2. {3. School of Cyberspace Security Beijing University of Posts and TelecommunicationsBeijingChina School of computer Science and technology Harbin Institute of Technology(Shenzhen)ShenzhenChina institute for Sustainable Industries and Liveable Cities Victoria UniversityMelbourneAustralia Key Laboratory of Tropical Translational Medicine of Ministry of Education Hainan Medical UniversityHaikouChina NHC Key Laboratory of Control of Tropical Diseases Hainan Medical UniversityHaikouChina Cyberspace Security research center Peng Cheng LaboratoryShenzhenChina

Objective:To determine the most influential data features and to develop machine learning approaches that best predict hospital readmissions among patients with ***:In this retrospective cohort study,we surveyed patient statistics and performed feature analysis to identify the most influential data features associated with *** of all-cause,3.-day readmission outcomes were modeled using logistic regression,artificial neural network,and Easy Ensemble.F1 statistic,sensitivity,and positive predictive value were used to evaluate the model ***:We identified 14 most influential data features(4 numeric features and 10 categorical features)and evaluated 3.machine learning models with numerous sampling methods(oversampling,undersampling,and hybrid techniques).The deep learning model offered no improvement over traditional models(logistic regression and Easy Ensemble)for predicting readmission,whereas the other two algorithms led to much smaller differences between the training and testing ***:Machine learning approaches to record electronic health data offer a promising method for improving readmission prediction in patients with *** more work is needed to construct datasets with more clinical variables beyond the standard risk factors and to fine-tune and optimize machine learning models.

关键词： Electronic health records Hospital readmissions Feature analysis Predictive models Imbalanced learning Diabetes

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A Measurement Study on Resource Popularity and Swarm Evolution of BitTorrent System

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International Journal of Communications, network and System Sciences 2013年第6期6卷 300-308页

作者： Majing Su Hongli zhang binxing {2. Lin Ye School of computer Science and technology Harbin Institute of Technology Harbin China

Analyzing and modeling of the BitTorrent (BT) resource popularity and swarm evolution is important for better understanding current BT system and designing accurate BT simulators. Although lots of measurement studies on BT almost cover each important aspect, little work reflects the recent development of BT system. In this paper, we develop a hybrid measurement system incorporating both active and passive approaches. By exploiting DHT (Distribute Hash Table) and PEX (Peer Exchange) protocols, we collect more extensive information compared to prior measurement systems. Based on the measurement results, we study the resource popularity and swarm evolution with different population in minute/ hour/day scales, and discover that: 1) the resources in BT system appear obvious unbalanced distribution and hotspot phenomenon, in that 74.6% torrents have no more than 1000 peers;2. The lifetime of torrents can be divided into a fast growing stage, a dramatically shrinking stage, a sustaining stage and a slowly fading out stage in terms of swarm population;3. Users’ interest and diurnal periodicity are the main factors that influence the swarm evolution. The former dominates the first two stages, while the latter is decisive in the third stage. We raise an improved peer arrival rate model to describe the variation of the swarm population. Comparison results show that our model outperforms the state-of-the-art approach according to root mean square error and correlation coefficient.

关键词： P2. BitTorrent Measurement Modeling Popularity Swarm Evolution

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BGP安全事件快速检测框架的设计与实现

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智能计算机与应用 2020年第7期10卷 34-39页

作者：霍峻杰张宇方滨兴哈尔滨工业大学计算机科学与技术学院哈尔滨150001

边界网关协议(BGP)在路由交换、网络互连领域具有不可替代的重要意义,是世界网络互通的基础核心。BGP安全事件具有影响力广、危害大等特点,需要及时发现BGP安全事件并进行排查修复。但是目前针对其设计的检测框架,数据处理慢,检测时延长... 详细信息

边界网关协议(BGP)在路由交换、网络互连领域具有不可替代的重要意义,是世界网络互通的基础核心。BGP安全事件具有影响力广、危害大等特点,需要及时发现BGP安全事件并进行排查修复。但是目前针对其设计的检测框架,数据处理慢,检测时延长,安全事件无法复现。基于以上需求,本文首先设计并提出了BGP安全事件快速检测框架,从结构、功能、运行和维护四个方面进行模块化设计,数据以管道流的形式流经各个模块;其次提出了基于分布式共享内存的BGP安全事件检测方案,并设计了可扩展的分布式历史数据库,用于对已发生的安全事件进行取证和复现;最后,对整个方案设计进行了初步实现部署,并对功能实现和性能指标进行了评估。

关键词： BGP安全分布式模块化

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