A Transductive Scheme Based Inference Techniques for Network Forensic Analysis

Network forensics is a security infrastructure,and becomes the research focus of forensic investigation.However many challenges still exist in conducting network forensics:network has produced large amounts of data;the comprehensibility of evidence extracting from collected data;the efficiency of evidence analysis methods,etc.To solve these problems,in this paper we develop a network intrusion forensics system based on transductive scheme that can detect and analyze efficiently computer crime in networked environments,and extract digital evidence automatically.At the end of the paper,we evaluate our method on a series of experiments on KDD Cup 1999 dataset.The results demonstrate that our methods are actually effective for real-time network forensics,and can provide comprehensible aid for a forensic expert.     

E-Healthcare Supported by Big Data

The era of open information in healthcare has arrived. E-healthcare supported by big data supports the move toward greater transparency in healthcare by making decades of stored health data searchable and usable. This paper gives an overview the e-healthcare architecture. We discuss the four layers of the architecture—data collection, data transport, data storage, and data analysis—as well as the challenges of data security, data privacy, real-time delivery, and open standard interface. We discuss the necessity of establishing an impeccably secure access mechanism and of enacting strong laws to protect patient privacy.     

Load Balancing Fat-Tree on Long-Lived Flows： Avoiding Congestion in a Data Center Network

In a data center network(DCN),load balancing is required when servers transfer data on the same path.This is necessary toavoid congestion.Load balancing is challenged by the dynamic transferral of demands and complex routing control.Because ofthe distributed nature of a traditional network,previous research on load balancing has mostly focused on improving the perfor-mance of the local network;thus,the load has not been optimally balanced across the entire network.In this paper,we propose anovel dynamic load-balancing algorithm for fat-tree.This algorithm avoids congestions to the great possible extent by searching fornon-conflicting paths in a centralized way.We implement the algorithm in the popular software-defined networking architectureand evaluate the algorithm’s performance on the Mininet platform.The results show that our algorithm has higher bisection band-width than the traditional equal-cost multi-path load-balancing algorithm and thus more effectively avoids congestion.     

Unveiling the Challenges in Improving Data Availability in Vehicular Networks with Network Coding

Retrieving data from mobile source vehicles is a crucial routine operation for a wide spectrum of vehicular network applications, in-cluding road surface monitoring and sharing. Network coding has been widely exploited and is an effective technique for diffusing in-formation over a network. The use of network coding to improve data availability in vehicular networks is explored in this paper. Withrandom linear network codes, simple replication is avoided, and instead, a node forwards a coded block that is a random combinationof all data received by the node. We use a network-coding-based approach to improve data availability in vehicular networks. To deter-mine the feasibility of this approach, we conducted an empirical study with extensive simulations based on two real vehicular GPStraces, both of which contain records from thousands of vehicles over more than a year. We observed that, despite significant improve-ment in data availability, there is a serious issue with linear correlation between the received codes. This reduces the data-retrievalsuccess rate. By analyzing the real vehicular traces, we discovered that there is a strong community structure within a real vehicularnetwork. We verify that such a structure contributes to the issue of linear dependence. Then, we point out opportunities to improve thenetwork-coding-based approach by developing community-aware code-distribution techniques.     

Background knowledge based privacy metric model for online social networks

The data of online social network （OSN） is collected currently by the third party for various purposes. One of the problems in such practices is how to measure the privacy breach to assure users. The recent work on OSN privacy is mainly focus on privacy-preserving data publishing. However, the work on privacy metric is not systematic but mainly focus on the traditional datasets. Compared with the traditional datasets, the attribute types in OSN are more diverse and the tuple is relevant to each other. The retweet and comment make the graph character of OSN notably. Furthermore, the open application programming interfaces （APIs） and lower register barrier make OSN open environment, in which the background knowledge is more easily achieved by adversaries. This paper analyzes the background knowledge in OSN and discusses its characteristics in detail. Then a privacy metric model faces OSN background knowledge based on kernel regression is proposed. In particular, this model takes the joint attributes and link knowledge into consideration. The effect of different data distributions is discussed. The real world data set from weibo.com has been adopted. It is demonstrated that the privacy metric algorithm in this article is effective in OSN privacy evaluation. The prediction error is 30% lower than that of the work mentioned above     

3patial Load Balancing in Nide-Area Wireless Networks

Load balancing is typically used in the frequency domain of cellular wireless networks to balance paging, access, and traffic load across the available bandwidth. In this paper, we extend load balancing into the spatial domain, and we develop two approaches-network load balancing and single-carrier multilink-for spatial load balancing. Although these techniques are mostly applied to cellular wireless networks and Wi-Fi networks, we show how they can be applied to EV-DO, a 3G cellular data network. When a device has more than one candidate server, these techniques can be used to determine the quality of the channel between a server and the device and to determine the load on each server. The proposed techniques leverage the advantages of existing EV-DO network architecture and are fully backward compatible. Network operators can substantially increase network capacity and improve user experience by using these techniques. Combining load balancing in the frequency and spatial domains improves connectivity within a network and allows resources to be optimally allocated according to the p-fair criterion. Combined load balancing further improves performance.     

大规模以数据为中心的网络架构下基于拓扑的数据分发机制(英文)

Massive information flows are generated from interactive processing and visualizations.To efficiently support information transmission over the Internet,information centric architecture has been recently proposed.In this paper,we consider an information centric architecture,called the data centric networking architecture to provide communication services to big data,where a service identifier is used to name the data objects.We propose different approaches for the dissemination of data objects in a large-scale data centric network.In particular,we propose various approaches to link the data dissemination approach with the topology of the Internet.Further,we evaluate the proposed approaches with respect to data delivery efficiency,round-trip time improvement,and deployment cost.Based on the results obtained from this study,it can be shown that by disseminating data objects to small ISPs,the data delivery efficiency can be significantly improved within an acceptable deployment cost.     

Systematic energy-balanced cooperative transmission scheme in wireless sensor networks

Energy efficiency is a critical issue in wireless sensor networks(WSNs).In order to minimize energy consumption and balance energy dissipation throughout the whole network,a systematic energy-balanced cooperative transmission scheme in WSNs is proposed in this paper.This scheme studies energy efficiency in systematic view.For three main steps,namely nodes clustering,data aggregation and cooperative transmission,corresponding measures are put forward to save energy.These measures are well designed and tightly coupled to achieve optimal performance.A half-controlled dynamic clustering method is proposed to avoid concentrated distribution of cluster heads caused by selecting cluster heads randomly and to get high spatial correlation between cluster nodes.Based on clusters built,data aggregation,with the adoption of dynamic data compression,is performed by cluster heads to get better use of data correlation.Cooperative multiple input multiple output(CMIMO) with an energy-balanced cooperative cluster heads selection method is proposed to transmit data to sink node.System model of this scheme is also given in this paper.And simulation results show that,compared with other traditional schemes,the proposed scheme can efficiently distribute the energy dissipation evenly throughout the network and achieve higher energy efficiency,which leads to longer network lifetime span.By adopting orthogonal space time block code(STBC),the optimal number of the cooperative transmission nodes varying with the percentage of cluster heads is also concluded,which can help to improve energy efficiency by choosing the optimal number of cooperative nodes and making the most use of CMIMO.     

Auxiliary Fault Location on Commercial Equipment Based on Supervised Machine Learning

As the fundamental infrastructure of the Internet,the optical network carries a great amount of Internet traffic.There would be great financial losses if some faults happen.Therefore,fault location is very important for the operation and maintenance in optical networks.Due to complex relationships among each network element in topology level,each board in network element level,and each component in board level,the con-crete fault location is hard for traditional method.In recent years,machine learning,es-pecially deep learning,has been applied to many complex problems,because machine learning can find potential non-linear mapping from some inputs to the output.In this paper,we introduce supervised machine learning to propose a complete process for fault location.Firstly,we use data preprocessing,data annotation,and data augmenta-tion in order to process original collected data to build a high-quality dataset.Then,two machine learning algorithms(convolutional neural networks and deep neural networks)are applied on the dataset.The evaluation on commercial optical networks shows that this process helps improve the quality of dataset,and two algorithms perform well on fault location.     

Research on location privacy protection method of sensor-cloud base station

In view of the privacy security issues such as location information leakage in the interaction process between the base station and the sensor nodes in the sensor-cloud system, a base station location privacy protection algorithm based on local differential privacy(LDP) is proposed. Firstly, through the local obfuscation algorithm(LOA), the base station can get the data of the real location and the pseudo location by flipping a coin, and then send the data to the fog layer, then the obfuscation location domain set is obtained. Secondly, in order to reconstruct the location distribution of the real location and the pseudo location in the base station, the location domain of the base station is divided into several decentralized sub-regions, and a privacy location reconstruction algorithm(PLRA) is performed in each sub-region. Finally, the base station correlates the location information of each sub-region, and then uploads the data information containing the disturbance location to the fog node layer. The simulation results show that compared with the existing base station location anonymity and security technique(BLAST) algorithm, the proposed method not only reduce the algorithm’s running time and network delay, but also improve the data availability. So the proposed method can protect the location privacy of the base station more safely and efficiently.     

基于生成对抗网络的差分隐私数据发布方法

机器学习的飞速发展使其成为数据挖掘领域最有效的工具之一,但算法的训练过程往往需要大量的用户数据,给用户带来了极大的隐私泄漏风险.由于数据统计特征的复杂性及语义丰富性,传统隐私数据发布方法往往需要对原始数据进行过度清洗,导致数据可用性低而难以再适用于数据挖掘任务.为此,提出了一种基于生成对抗网络（Generative Adversarial Network,GAN）的差分隐私数据发布方法,通过在GAN模型训练的梯度上添加精心设计的噪声来实现差分隐私,确保GAN可无限量生成符合源数据统计特性且不泄露隐私的合成数据.针对现有同类方法合成数据质量低、模型收敛缓慢等问题,设计多种优化策略来灵活调整隐私预算分配并减小总体噪声规模,同时从理论上证明了合成数据严格满足差分隐私特性.在公开数据集上与现有方法进行实验对比,结果表明本方法能够更高效地生成质量更高的隐私保护数据,适用于多种数据分析任务.     

网络大数据安全问题及其解决对策研究

现如今的21世纪,随着“互联网+”的飞速发展,网络信息数据也不断开放,因此,21世纪也被誉为了计算机互联网时代。这也导致目前人们获取数据的最主要的途径已经开始转变为从互联网上获取数据。这也在一定条件上为网络大数据的应用和发展创造了非常有利的条件。但是,在近几年,随着互联网的不断发展,网络大数据的规模不断扩大,在数据的采集、存储以及共享方面发生了巨大的变化。这也暴露出了一些网络数据安全方面的问题,例如隐私保护问题等。面对现代日益信息化的社会,网络大数据安全问题已经成为目前影响网络发展的最大的“绊脚石”。因此,目前对网络大数据的安全问题进行深入的研究,并对其进行相关对策的探究,已经成为目前主要的任务。针对于此,文章对现代网络大数据安全问题及其解决对策进行了相关的研究,希望能为相关者提供一些借鉴的经验。     

个人移动数据收集中的多维轨迹匿名方法

在情景感知位置服务中,移动互联网络的开放性使得个人移动数据面临巨大的安全风险,移动数据的时空关联特性对个人数据的隐私保护提出重大挑战.针对基于时空关联的背景知识攻击,本文提出了一种多维的轨迹匿名隐私保护方法.该方法在匿名轨迹数据收集系统的基础上,基于多用户协作的隐私保护模式,通过时间匿名和空间匿名算法,实现用户的隐私保护.实验结果表明,该方法可以有效的对抗基于位置和移动方式的背景知识攻击,满足了k-匿名的隐私保护要求.     

MCDP:基于神经网络的多集群分布式差分隐私数据发布方法

大数据应用能够为人们的生活和工作方式提供便捷,但包含消费记录、社交关系、地理位置等个人隐私信息的数据在发布过程中可能被服务提供商收集,用户隐私面临巨大威胁.本文首次提出了一个基于神经网络的多集群分布式差分隐私数据发布方法,能够显著缓解单服务器的数据处理压力.同时,利用神经网络算法进行隐私参数预测明显提高了预测精度和预测效率,并且集群之间不同的隐私参数也保证了方案的灵活性.此外,由于中心服务器存储的是经过差分隐私处理后的统计数据,即使中心服务器由于遭受攻击导致存储的数据泄露,也能确保用户数据隐私.实验对比分析表明,我们的方法在隐私处理效率、隐私保护强度、预测精度和预测效率等方面都有明显优势.     

基于数据融合和数据挖掘技术的网络取证体系的研究

数据融合技术能利用不同传感数据的冗余信息实现互补以提高信息处理的正确性。数据挖掘技术能从大量数据中挖掘和发现有价值和隐含的知识,用于建模和优化。在分析了网络取证在网络安全领域的重要作用的基础上,结合数据融合和数据挖掘技术对网络取证系统进行了研究,并阐述其功能和组成、数据融合和推理决策的过程。     

分析数据加密技术在计算机网络通信安全中的应用

随着现代信息技术的高速发展,如今各个行业的不同领域对计算机网络通信技术的应用不断提高重视,而其中的安全问题也受到了全社会的广泛关注。作为数据信息传输的重要方式,计算机网络通信存在一定的安全风险,关系到大众的隐私信息及财产安全,所以需要加强对计算机网络通信安全方面的研究。而数据加密技术在提高计算机网络通信安全方面效果显著,文章便主要针对该技术展开分析,并对其在计算机网络通信安全中的具体应用提出几点看法,以期为相关从业者提供参考借鉴。     

隐私保持的决策树分类挖掘

 隐私保持的数据挖掘是目前数据挖掘领域的重要研究方向之一,其首要研究内容是开发在不泄露隐私数据的前提下进行数据挖掘的方法.决策树是分类挖掘的一种重要方法,也是目前隐私保持的数据挖掘领域中少有的被深入研究了的分类方法.针对目前尚未对隐私保持的决策树挖掘方法进行系统总结的问题,本文对该领域进行综述.首先对问题背景进行介绍,随后介绍了该领域的研究现状,对现有方法进行了分类和总结,最后总结出该领域进一步研究的方向.     

传感器网络中基于路线的隐私保护数据聚集算法

针对现有隐私保护数据聚集算法依赖某种网络拓扑结构和加解密次数过多的问题,本文提出了一种基于同心圆路线的隐私保护数据聚集算法PCIDA （Privacy-preserving and Concentric-circle Itinerary-based Data Aggregation algorithm）.PCIDA沿着设计好的理想路线执行数据聚集,使得算法不依赖网络拓扑结构.PCIDA利用安全通道保证数据的隐私性,避免了数据聚集过程中的加解密运算.PCIDA沿着同心圆并行处理,使得算法数据处理延迟较小.理论分析和实验结果显示,PCIDA在较低通信量和能耗的情况下获得较高的数据隐私性和聚集精确度.     

Data Protection and Data Sharing in Telematics

Automotive telematics may be defined as the information-intensive applications enabled for vehicles by a combination of telecommunications and computing technology. Telematics by its nature requires the capture, storage, and exchange of sensor data to obtain remote services. Such data likely include personal, sensitive information, which require proper handling to protect the driver's privacy. Some existing approaches focus on protecting privacy through anonymous interactions or by stopping information flow altogether. We complement these by concentrating instead on giving different stakeholders control over data sharing and use. In this paper, we identify several data protection challenges specifically related to the automotive telematics domain, and propose a general data protection framework to address some of those challenges. The framework enables data aggregation before data is released to service providers, which minimizes the disclosure of privacy sensitive information. We have implemented the core component, the privacy engine, to help users manage their privacy policies and to authorize data requests based on policy matching. The policy manager provides a flexible privacy policy model that allows data subjects to express rich constraint-based policies, including event-based, and spatio-temporal constraints. Thus, the policy engine can decide on a large number of requests without user assistance and causes no interruptions while driving. A performance study indicates that the overhead is stable with an increasing number of data subjects.     

软件定义数据中心网络研究

提出了一种基于软件定义网络的数据中心网络方案,并对其结构和部署进行了详细介绍。通过使用软件定义网络技术,该方案解决了传统数据中心中网络利用效率低下、管控方式复杂、建设运维成本较高等问题。该方案具有结构简单、易于部署的优点。在负载均衡、域内虚拟机迁移等典型应用中,我们对该方案进行了验证,结果表明该方案具有良好的实用性。该方案为数据中心网络架构设计提供了一种重要思路。