移动自组网入侵检测综述

移动自组网是由移动节点自组织形成的网络,由于其动态拓扑、无线通信的特点,容易遭受各种安全威胁.移动自组网入侵检测是安全研究中最核心的技术之一.对现有的入侵检测体系结构进行了讨论,同时分析了各自的优点和缺点,接着较详细地介绍了各种检测方法,并进行了综合比较.最后指出移动自组网入侵检测研究今后发展的趋势.     

Mixing local and global information for community detection in large networks

Clustering networks play a key role in many scientific fields, from Biology to Sociology and Computer Science. Some clustering approaches are called global because they exploit knowledge about the whole network topology. Vice versa, so-called local methods require only a partial knowledge of the network topology. Global approaches yield accurate results but do not scale well on large networks; local approaches, vice versa, are less accurate but computationally fast. We propose CONCLUDE (COmplex Network CLUster DEtection), a new clustering method that couples the accuracy of global approaches with the scalability of local methods. CONCLUDE generates random, non-backtracking walks of finite length to compute the importance of each edge in keeping the network connected, i.e., its edge centrality. Edge centralities allow for mapping vertices onto points of a Euclidean space and compute all-pairs distances between vertices; those distances are then used to partition the network into clusters.     

A framework for joint community detection across multiple related networks

Community detection in networks is an active area of research with many practical applications. However, most of the early work in this area has focused on partitioning a single network or a bipartite graph into clusters/communities. With the rapid proliferation of online social media, it has become increasingly common for web users to have noticeable presence across multiple web sites. This raises the question whether it is possible to combine information from several networks to improve community detection. In this paper, we present a framework that identifies communities simultaneously across different networks and learns the correspondences between them. The framework is applicable to networks generated from multiple web sites as well as to those derived from heterogeneous nodes of the same web site. It also allows the incorporation of prior information about the potential relationships between the communities in different networks. Extensive experiments have been performed on both synthetic and real-life data sets to evaluate the effectiveness of our framework. Our results show superior performance of simultaneous community detection over three alternative methods, including normalized cut and matrix factorization on a single network or a bipartite graph.     

Theorizing mobility in community networks

Community networks emerged in North America during the late 1970s and early 1980s. During the past three decades, paradigms for networked information, services, and collaboration as resources for community development have evolved in many respects. In this paper we revisit a theoretical analysis of broadband Internet community networks [Carroll, J.M., Rosson, M.B., 2003a. A trajectory for community networks. The Information Society 19(5), 381–393], and extend that analysis to mobile/wireless community networks. This analysis is part of the planning for a specific municipal wireless project in the town of State College, Pennsylvania. But more broadly, it is intended to engage and to help focus human–computer interaction (HCI) design perspectives in the development of wireless community networks throughout North America and elsewhere.     

移动社交网络异常签到在线检测算法

随着智能手机、Pad等智能移动设备的广泛普及,移动社交网络的应用得到了快速发展。本文针对移动社交网络中用户异常签到位置检测问题,提出了一类基于用户移动行为特征的异常签到在线检测方法。首先,在基于距离的异常模型基础上,提出了基于历史位置（H-Outlier）和基于好友圈（F-Outlier）两种异常签到模型;然后,针对H-Outlier提出了一种优化的检测算法H-Opt,利用所提的签到状态模型与优化的邻居搜索机制降低检测时间;针对F-Outlier提出了一种基于触发的优化检测算法F-Opt,将连续的在线异常检测转化成了基于触发的异常检测方式;最后,在真实的移动社交网络用户签到数据集上,验证了所提算法的有效性。实验结果显示,F-Opt显著降低了H-Opt的异常检测错误率;同时,相比于LUE算法,F-Opt和H-Opt的效率分别平均提升了2.34倍和2.45倍。     

基于机器学习的移动自组织网络入侵检测方法

移动自组织网络是由无线移动节点组成的复杂分布式通信系统。研究了移动自组织网络的入侵检测问题,采用了一种新型的基于机器学习算法的异常入侵检测方法。该方法获取正常事件的内部特征的相互关系模式,并将该模式作为轮廓检测异常事件。在Ad hoc 按需距离向量协议上实现了该方法,并在网络仿真软件QualNet中对其进行了评估。     

基于K-means聚类和数据场理论的复杂网络社团结构探寻

探寻社团结构是研究复杂网络结构与功能之间关系的基础.提出和分析了基于K-means聚类的社团探寻算法和基于数据场理论的社团探寻算法,并通过实验仿真验证了这两种算法的有效性.在仿真中发现并验证了社团内部比整个网络具有更加鲜明的小世界效应,这说明在网络控制中,在相同的耦合强度下,对社团的同步控制比对整个鲻网络的同步控制更容易实现.     

Detecting proximity events in sensor networks

Sensor networks are often used to perform monitoring tasks, such as animal and vehicle tracking, or the surveillance of enemy forces in military applications. In this paper we introduce the concept of proximity queries, which allow us to report interesting events, observed by nodes in the network that lie within a certain distance from each other. An event is triggered when a user-programmable predicate is satisfied on a sensor node. We study the problem of computing proximity queries in sensor networks and propose several alternative techniques that differ in the number of messages exchanged by the nodes and the quality of the returned answers. Our solutions utilize a distributed routing index, maintained by the nodes in the network, that is dynamically updated as new observations are obtained by the nodes. This distributed index allows us to efficiently process multiple proximity queries involving several different event types within a fraction of the cost that a straightforward evaluation requires. We present an extensive experimental study to show the benefits of our techniques under different scenarios using both synthetic and real data sets. Our results demonstrate that our algorithms scale better and require significantly fewer messages compared to a straightforward execution of the queries.     

基于级联卷积网络的自然场景下的车牌检测

为有效解决自然场景下车牌检测的分类效率和分类准确率之间的矛盾,提出一种鲁棒的快速车牌检测方法。以整个车牌作为训练和检测的基本单位,直接使用卷积网络作为检测器,级联多个卷积网络,对自然场景下的车牌进行检测。通过引入卷积网络作为基本的学习子单元,进一步把多个子单元组织成级联结构,满足了自然场景下的车牌检测对检测准确率和检测效率的要求。在现实数据上的实验结果表明,级联卷积网络在保证较低误报率的同时具有较高的检测率和检测效率。     

Detecting community structure in complex networks using genetic algorithm based on object migrating automata

Community structure is an important topological feature of complex networks. Detecting community structure is a highly challenging problem in analyzing complex networks and has great importance in understanding the function and organization of networks. Up until now, numerous algorithms have been proposed for detecting community structure in complex networks. A wide range of these algorithms use the maximization of a quality function called modularity. In this article, three different algorithms, namely, MEM-net, OMA-net, and GAOMA-net, have been proposed for detecting community structure in complex networks. In GAOMA-net algorithm, which is the main proposed algorithm of this article, the combination of genetic algorithm (GA) and object migrating automata (OMA) has been used. In GAOMA-net algorithm, the MEM-net algorithm has been used as a heuristic to generate a portion of the initial population. The experiments on both real-world and synthetic benchmark networks indicate that GAOMA-net algorithm is efficient for detecting community structure in complex networks.     

Community detection from biological and social networks: A comparative analysis of metaheuristic algorithms

In order to analyze complex networks to find significant communities, several methods have been proposed in the literature. Modularity optimization is an interesting and valuable approach for detection of network communities in complex networks. Due to characteristics of the problem dealt with in this study, the exact solution methods consume much more time. Therefore, we propose six metaheuristic optimization algorithms, which each contain a modularity optimization approach. These algorithms are the original Bat Algorithm (BA), Gravitational Search Algorithm (GSA), modified Big Bang–Big Crunch algorithm (BB-BC), improved Bat Algorithm based on the Differential Evolutionary algorithm (BADE), effective Hyperheuristic Differential Search Algorithm (HDSA) and Scatter Search algorithm based on the Genetic Algorithm (SSGA). Four of these algorithms (HDSA, BADE, SSGA, BB-BC) contain new methods, whereas the remaining two algorithms (BA and GSA) use original methods. To clearly demonstrate the performance of the proposed algorithms when solving the problems, experimental studies were conducted using nine real-world complex networks − five of which are social networks and the rest of which are biological networks. The algorithms were compared in terms of statistical significance. According to the obtained test results, the HDSA proposed in this study is more efficient and competitive than the other algorithms that were tested.     

Distributed Court System for intrusion detection in mobile ad hoc networks

Securing routing layer functions in mobile ad hoc networks is an important issue, which includes many challenges like how to enhance detection accuracy when facing the highly dynamic characteristic of such networks, and how to distinguish malicious accusations under a totally autonomous structure. In this paper, we propose Distributed Court System (DCS), a complete Intrusion Detection System that intends to solve these challenges in a low-cost and robust way. We do not deploy any centralized entity, but rely on the collaboration among the nodes neighbouring the suspected node, to integrate information, improve the detection accuracy, and reject dissemination of malicious accusation. Through mathematical analysis and simulation, the proposed DCS is proved to be effective in a highly mobile and hostile network environment.     

Extent of private information disclosure on online social networks: An exploration of Facebook mobile phone users

The present study adopts the Communication Privacy Management theory and investigates the factors that influence the extent of private information disclosure of Facebook mobile phone users. Using a sample size of 488 adult mobile phone users, the study further investigates the differential impact of age on the extent of private information disclosure. Results from the logistic regressions run reveal that use of smartphones to access social networking sites, use of multiple social networks, and being female decrease the likelihood of private information disclosure. In addition, usability problems increase the likelihood of information disclosure by older adults. The analyses show no association between perceived benefit and private information disclosure.     

Distributed debugging for mobile networks

Debuggers are an integral part, albeit often neglected, of the development of distributed applications. Ambient-oriented programming (AmOP) is a distributed paradigm for applications running on mobile ad hoc networks. In AmOP the complexity of programming in a distributed setting is married with the network fragility and open topology of mobile applications. To our knowledge, there is no debugging approach that tackles both these issues. In this paper we argue that a novel kind of distributed debugger that we term an ambient-oriented debugger, is required. We present REME-D (read as remedy), an online ambient-oriented debugger that integrates techniques from distributed debugging (event-based debugging, message breakpoints) and proposes facilities to deal with ad hoc, fragile networks – epidemic debugging, and support for frequent disconnections.     

Triangle-based routing for mobile ad hoc networks

Routing protocols for Mobile ad hoc networks (MANETs) have been studied extensively in the past decade. Routing protocols for MANETs can be broadly classified as reactive (on-demand), proactive, hybrid and position-based. Reactive routing protocols are attractive because a route between a source and a destination is established only when it is needed. Such protocols, unlike proactive protocols, do not have high overhead for route maintenance and are especially suitable for networks in which not all nodes communicate frequently. One problem with existing reactive routing protocols is the propagation of redundant route request messages during route discovery. In this paper, we present a low-overhead reactive routing protocol which reduces propagation of redundant route request messages. We also compare its performance with the well-known reactive routing protocol AODV.     

Real-time data dissemination in mobile peer-to-peer networks

Mobile peer-to-peer networks have found many uses such as streaming of audio and video data. There are circumstances, such as emergency situations and disaster recovery, when real-time delivery is a fundamental requirement. The problem is challenging due to the limited network capacity, the variable transmission rates and the unpredictability with respect to the network conditions in the mobile peer-to-peer network.In this paper we address the problem of real-time data dissemination of multimedia streams in mobile peer-to-peer networks. Four routing algorithms are proposed based on a packet's deadline, priority or a combination of these metrics. They are simulated under different setups in a mobile peer-to-peer network with Bluetooth connectivity and nodes broadcasting audio and video streams using different priorities. We compare the performance of the algorithms using a number of metrics. Detailed experimental results are presented. Based on these results, propositions on the usage of the algorithms and the design of network requirements are presented.     

Detecting discussion communities on vaccination in twitter

Vaccines have contributed to dramatically decrease mortality from infectious diseases in the 20th century. However, several social discussion groups related to vaccines have emerged, influencing the opinion of the population about vaccination for the past 20 years. These communities discussing on vaccines have taken advantage of social media to effectively disseminate their theories. Nowadays, recent outbreaks of preventable diseases such as measles, polio, or influenza, have shown the effect of a decrease in vaccination rates. Social Networks are one of the most important sources of Big Data. Specifically, Twitter generates over 400 million tweets every day. Data mining provides the necessary algorithms and techniques to analyse massive data and to discover new knowledge. This work proposes the use of these techniques to detect and track discussion communities on vaccination arising from Social Networks. Firstly, a preliminary analysis using data from Twitter and official vaccination coverage rates is performed, showing how vaccine opinions of Twitter users can influence over vaccination decision-making. Then, algorithms for community detection are applied to discover user groups opining about vaccines. The experimental results show that these techniques can be used to discover social discussion communities providing useful information to improve immunization strategies. Public Healthcare Organizations may try to use the detection and tracking of these social communities to avoid or mitigate new outbreaks of eradicated diseases.     

面向Android手机平台异常入侵检测的研究

智能手机应用普及的同时,入侵的危害也越来越严重。针对Android智能手机平台,结合入侵检测的相关研究,解决智能手机入侵检测的问题。采取在Android平台下采集系统和网络特征数据,上传至远程云服务器,在服务器上利用SVM进行分析处理,以给出合理的入侵与否的判断,进而尽快更新手机的处理机制。实验结果表明,既减少了智能手机资源消耗,又能对手机的异常入侵尽快做出反应和处理。