Analysis of malware propagation behavior in Social Internet of Things

Social Internet of Things (SIoT) is an evolution of the Internet of Things, where objects interact socially with each other in the sense that they can independently establish new relationships, offer, or discover services, in order to accomplish their tasks with minimum involvement of the user. This additional convenience comes at the expense of higher risk of speeding up malware propagation through the dynamically created relationships. Because of the undesirable effects of malware (eg, disruption of device operation), it is essential to understand their spreading behavior in order to minimize their negative impacts. In this paper, we analyze malware propagation behavior in SIoT and investigate different parameters that influence spreading of malware. Toward that end, a simulator has been developed to simulate the spreading process of malware in SIoT. Many propagation scenarios were analyzed using the proposed simulator. Simulation results show that adding more relationships in the SIoT or increasing the number of owned objects per user has increased malware spreading rate. For example, the time to infect all objects is faster by 45% when objects communicate through four relationships compared with the case when objects communicate through only two relationships in SIoT. We also investigated ways to restrict the malware spreading. Results show that preventing objects from establishing dynamic social relationship slows down the infection by 40% compared with the next best scenario (ie, blocking co‐location relationships), which means more time for vendors to patch up their products.     

A scheme of access service recommendation for the Social Internet of Things

The rapid increase in the complexity and the extent of personalization of services in the Internet of Things (IoT) has led to a greater demand for frequent collaboration among heterogeneous devices. Moreover, with the inseparable relations between human and devices, the paradigm of Social IoT (SIoT) is gaining popularity in recent years. How to effectively facilitate the access to quality services and credible devices in large‐scale networks via defining, establishing, and managing social architectures among things has become a critical issue. In this paper, a scheme of access service recommendation for the SIoT is presented with the understanding of inherent constraints and factors that influence the security and stability of IoT networks. In which, timeliness properties are considered in each transaction for dynamic performance enhancements. With the benefits of promoting service discovery and composition, social relationships among things are introduced in the proposed scheme. An energy‐aware mechanism is also utilized as a restrictive factor in trustworthiness evaluation. Finally, the recommendation is based not only on the past performance but also on the social relationship and the energy status of nodes. Simulation experiments demonstrate the effectiveness and benefits of our scheme from three aspects including rating accuracy, dynamic behavior, and network stability. Copyright © 2015 John Wiley & Sons, Ltd.     

基于区块链的社会物联网可信服务管理框架

针对当前社会物联网可信服务管理中存在的中心化程度高、交易不透明和易受攻击等问题,提出了一种基于区块链的社会物联网可信服务管理框架。该框架通过区块链的去中心化特性在服务请求者和服务提供者之间直接建立信任关系,利用智能合约产生并管理新的交易,实现交易过程透明化并减少管理维护成本。同时,区块链的共识机制可用于交易验证并防范恶意攻击和篡改,减少网络攻击和恶意节点的欺骗。最后,分析了该框架在实际构建中面临的主要挑战。     

使用深度学习的社交物联网信任预测模型

社交物联网是社交网络概念在物联网中整合后兴起的一个蓬勃发展的研究领域。提出了一种适用于社交物联网网络的改进型节点级信任模型,并通过与其他信任模型的对比仿真实验证明在恶意节点的攻击下,提出的模型拥有更好的稳定性和适用性,总体波动较小。同时,针对实际社交物联网网络中新加入网络的陌生节点可能遇到的网络延迟影响信任值评估的问题,在改进型节点级信任模型的基础上进一步使用了深度学习模型对其进行信任值预测。仿真证明,使用深度学习预测后模型的系统性能明显优于不使用深度学习的模型,成功交互率提升约1.8%。     

Applying Hebbian Theory to Enhance Search Performance in Unstructured Social‐Like Peer‐to‐Peer Networks

Unstructured peer‐to‐peer (p2p) networks usually employ flooding search algorithms to locate resources. However, these algorithms often require a large storage overhead or generate massive network traffic. To address this issue, previous researchers explored the possibility of building efficient p2p networks by clustering peers into communities based on their social relationships, creating social‐like p2p networks. This study proposes a social relationship p2p network that uses a measure based on Hebbian theory to create a social relation weight. The contribution of the study is twofold. First, using the social relation weight, the query peer stores and searches for the appropriate response peers in social‐like p2p networks. Second, this study designs a novel knowledge index mechanism that dynamically adapts social relationship p2p networks. The results show that the proposed social relationship p2p network improves search performance significantly, compared with existing approaches.     

Evaluating Twitter Data to Discover User’s Perception About Social Internet of Things

Social Internet of Things (SIoT) is a young paradigm that integrates Internet of Things and Social Networks. Social Internet of Things is defined as a social network of intelligent objects. SIoT has led to autonomous decision making and communication between object peers. SIoT has created and opened many research avenues in the recent years and it is vital to understand the impact of SIoT in the real world. In this paper, we have mined twitter to evaluate the user awareness and impact of SIoT among the public. We use R for mining twitter and perform extensive sentiment analysis using supervised and semi supervised algorithms to evaluate the user’s perception about SIoT. Experimental results show that the proposed Fragment Vector model, a semi supervised classification algorithm is better when compared to supervised classification algorithms namely Improved Polarity Classifier (IPC) and SentiWordNet Classifier (SWNC). We also evaluate the combined performance of IPC and SWNC and propose a hybrid classifier (IPC?+?SWNC). Our analysis was challenged by limited number of tweets with respect to our study. Experimental results using R has produced evidences of its social influences.     

A new 4G architecture providing multimode terminals always best connected services

In recent years, wireless communication technology has undergone a tremendous change. Various radio access technologies have been deployed all over the world. The 4G mobile system was proposed to integrate all of these radio access technologies into a common network called the open wireless architecture (OWA) platform. As one of the main features of a 4G mobile system, always best connected (ABC) services enable users to choose the best available access networks in a way that best suits their needs. A new architecture capable of supporting ABC service is proposed in this study. There are three parts to the proposed architecture. First, a novel access discovery mechanism that integrates service location protocol and location-based service is presented. Second, a new personalized network selection scheme is put forward. Users can select their personalized "best" network by changing weight factors and constraints in a single objective optimization problem. Third, a seamless handover mechanism based on Mobile IPv6 is proposed. The mechanism supports end-to-end quality of service. Through analysis, this architecture demonstrates that it has benefits not only for network operators, but also for users     

一种基于本体语义的网格服务发现模型

在网格环境中,网格服务的发现机制对于系统的性能和效率有着重要的意义和作用。目前网格服务发现模型多数是集中式的、基于关键字的服务匹配模型,缺乏灵活性和推理能力,发现结果难以令人满意。提出了一种基于本体论语义和Agent代理的网格服务发现模型。采用Agent代理技术进行服务搜索,不同的代理能够提供不同领域的本体知识库,从而达到多领域的服务发现;利用本体论语义进行服务的匹配,从而达到智能化的服务发现。该模型提高了服务发现的准确率,同时也增强了系统的可维护性、灵活性和扩展性以及服务管理的自治性和容错性。     

Unsupervised regions based segmentation using object discovery

We present a new unsupervised algorithm to discovery and segment out common objects from multiple images. Compared with previous cosegmentation methods, our algorithm performs well even when the appearance variations in the foregrounds are more substantial than those in some areas of the backgrounds. Our algorithm mainly includes two parts: the foreground object discovery scheme and the iterative region allocation algorithm. Two terms, a region-saliency prior and a region-repeatness measure, are introduced in the foreground object discovery scheme to detect the foregrounds without any supervisory information. The iterative region allocation algorithm searches the optimal solution for the final segmentation with the constraints from a maximal spanning tree, and an effective color-based model is utilized during this process. The comparative experimental results show that the proposed algorithm matches or outperforms several previous methods on several standard datasets.     

M2LSH:基于LSH的高维数据近似最近邻查找算法

在许多应用中,LSH(Locality Sensitive Hashing)以及各种变体,是解决近似最近邻问题的有效算法之一.虽然这些算法能够很好地处理分布比较均匀的高维数据,但从设计方案来看,都没有针对数据分布不均匀的情况做相应的优化.针对这一问题,本文提出了一种新的基于LSH的解决方案(M2LSH,2 Layers Merging LSH),对于数据分布不均匀的情况依然能得到一个比较好的查询效果.首先,将数据存放到具有计数功能的组合哈希向量表示的哈希桶中,然后通过二次哈希将这些桶号投影到一维空间,在此空间根据各个桶中存放的数据个数合并相邻哈希桶,使得新哈希桶中的数据量能够大致均衡.查询时仅访问有限个哈希桶,就能找到较优结果.本文给出了详细的理论分析,并通过实验验证了M2LSH的性能,不仅能减少访问时间,也可提高结果的正确率.     

Cooperation of Network and Service Layer in Mobile Ad hoc Networks

In this article, we investigate how a service discovery protocol at the service layer in Mobile Ad Hoc Networks can be improved, by utilizing routing information from the network layer. We consider in this article proactive routing protocols as basis for the underlying routing mechanism, and focus on the optimization of proactive service discovery protocols. First we introduce a general framework, enabling the cooperation of service layer and network layer, and then we propose the optimization scheme. The performance evaluation done by simulation shows that the proposed cooperation scheme can optimize the service discovery protocol, i.e. reduce the communication cost of service discovery, by utilizing routing table and topology information.     

基于改进蚁群算法的可信服务发现

针对非结构化P2P网络中的服务发现问题,提出了一种基于改进蚁群算法的可信服务发现方法。该方法在传统蚁群算法基础上应用若干新的策略控制蚁群的行为,如动态蚁群策略、子蚂蚁策略、服务节点的信誉评估策略以及蚂蚁的恶意节点惩罚策略等, 高信誉服务节点在蚂蚁的推荐下进行邻居更换和自组织。 这些策略和方法为蚁群算法在分布式环境下服务发现的应用研究提供了一种新思路。实验结果表明, 本算法在各种常见复杂网络环境下仍可保证较高的可信服务查准率和更高的综合服务发现效率, 通过对实验数据的分析和解释得到了若干有益的结论,为进一步的研究工作奠定了基础。     

Modeling the Ambient Intelligence Application System: Concept, Software, Data, and Network

General reference models are essential for the ambient intelligence (AmI) system design, and they should be clear and simple both in terms of physical structure and software architecture. In this paper, we introduce a conceptual system model. According to this model, we present a physical structure model and a multiagent system based software architecture in the following step. We also give a data management mechanism model for the AmI environment. To evaluate our models, we implement them in a prototype ldquoSmart-Ovenrdquo system. Our models are suitable for rapid system modeling and development; they can help the designers and the programmers develop new AmI applications more rapidly and conveniently. Using the simplified AmI physical structure model, the physical AmI supporting environment can be established easily and rapidly. Via the software architecture and data management model, AmI application systems achieve flexibility, service discovery, and interoperability, which are the most common technical limitations in other related systems. Furthermore, our models can also help the terminal users configure their own AmI system by themselves. As a result of the works carried out in this paper, one can simplify the procedures and enhance the efficiency of AmI applications system design and development.     

基于兴趣和行为预测的移动社交网络动态资源发现机制

针对时延容忍的移动社交网络中的资源发现问题,在三维环境下提出一种基于兴趣和行为预测的动态资源发现机制(IBRD)。IBRD首先从用户的文件资源和信息表中提取兴趣向量,然后通过节点间的余弦相似度计算构造初始的虚拟兴趣社区。之后,通过对移动社交数据的分析,建立半马尔可夫链模型以预测节点的行为和运动轨迹。依据模型的预测结果,实现虚拟兴趣社区的动态维护。最后,基于动态的虚拟兴趣社区构建高效的资源发现策略。IBRD机制在随机网络仿真器(ONE)上得以实现,实验结果表明,IBRD与同类模型相比,具有较高的资源发现成功率、较低的平均时延和通信开销。     

HANDY: a hybrid association rules mining approach for network layer discovery of services for mobile ad hoc network

Mobile ad hoc network (MANET) is an infrastructure-less network formed between a set of mobile nodes. The discovery of services in MANET is a challenging job due to the unique properties of network. In this paper, a novel service discovery framework called hybrid association rules based network layer discovery of services for ad hoc networks (HANDY) has been proposed. HANDY provides three major research contributions. At first, it adopts a cross-layer optimized design for discovery of services that is based on simultaneous discovery of services and corresponding routes. Secondly, it provides a multi-level ontology-based approach to describe the services. This resolves the issue of semantic interoperability among the service consumers in a scalable fashion. Finally, to further optimize the performance of the discovery process, HANDY recommends exploiting the inherent associations present among the services. These associations are used in two ways. First, periodic service advertisements are performed based on these associations. In addition, when a response of a service discovery request is generated, correlated services are also attached with the response. The proposed service discovery scheme has been implemented in JIST/SWANS simulator. The results demonstrate that the proposed modifications give rise to improvement in hit ratio of the service consumers and latency of discovery process.     

网格环境中教育资源发现机制

提出了网格环境下教育资源发现机制模型，将P2P技术引入网格资源发现中以实现分布式资源查找．提高其可扩展性，针对教育资源的特点，进行了基于类别的虚拟社区划分．对资源请求的查找可限定在所属虚拟社区内进行，从而可缩小搜索空间，提高资源的发现速度。     

基于子空间三角不等式的高维码字搜索算法

本文分析了码字搜索算法中基于均值、方差和范数的删除准则,指出基于方差和范数的删除准则之间存在冗余缺陷.在此基础上,提出了一种新的子空间三角不等式删除准则,根据子空间中码字与参考点之间的距离来排除候选码字.基于方差的删除准则可以看成是子空间三角不等式删除准则的特例.在新的删除准则中,通过选择合适的子空间参考点,能够排除更...     

On Semantic Caching and Query Scheduling for Mobile Nearest-Neighbor Search

Location-based services have received increasing attention in recent years. In this paper, we address the performance issues of mobile nearest-neighbor search, in which the mobile user issues a query to retrieve stationary service objects nearest to him/her. An index based on Voronoi Diagram is used in the server to support such a search, while a semantic cache is proposed to enhance the access efficiency of the service. Cache replacement policies tailored for the proposed semantic cache are examined. Moreover, several query scheduling policies are proposed to address the inter-cell roaming issues in multi-cell environments. Simulations are conducted to evaluate the proposed methods. The result shows that the system performance, in terms of cache hit ratio, query response time, cell-cross number and cell-recross number, is improved significantly.     

Trigger Detection Using Geographical Relation Graph for Social Context Awareness

The concept of context awareness is believed to be a key enabler for the new ubiquitous network service paradigm brought by cloud computing platforms and smartphone OSs. In particular, autonomous context-based service customization is becoming an essential tool in this context because users cannot be expected to pick step by step the appropriate network services by manually and explicitly matching preferences for their current context. In this work, we hence focus on the core problem of how to detect changes of context for network services. In turn, detection of such changes can trigger timely system reconfigurations. We introduce a trigger detection mechanism based on a mixed graph-based representation model able to encode geographical and social relationships among people and social objects like stores, restaurants, and event spots. Our mechanism generates a trigger when a significant change in the graph takes place, and it is able to render significant changes in a geographical relationship that holds among objects socially connected with each other. The main benefits of our method are that (1) it does not require building reference models in advance, and (2) it can deal with different kinds of social objects uniformly once the graph is defined. A computer simulation scenario provides evidence on the expected performance of our method.