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.     

无线传感器网络中恶意软件传播研究

从平面无线传感器网络的拓扑结构、无线共享通信及安全机制等固有特征出发,对无线传感器网络上的恶意软件传播动力学进行研究。首先,使用随机几何图建立平面无线传感器网络模型;然后,基于元胞自动机理论建立恶意软件SI（Susceptible—Infected）传播模型,该模型充分考虑无线传感器网络固有特征和传播特征,模型建立引入MAC机制和随机密钥预分布方案。分析和仿真表明,无线传感器网络的空间局域化结构特征、无线信道共享机制和安全管理应用主导了传播增长效果,限制了恶意软件传播速度,降低了在无线传感器网络中大规模流行恶意软件的风险。文中提出的模型能够描述无线传感器网络中恶意软件传播行为,为建立无线传感器网络安全防御机制提供了基础。     

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%。     

天基物联网：基本概念、体系架构及发展趋势

物联网技术的发展和天基网络的成熟催生了天基物联网的概念。国家新型基础设施建设的提出为天基物联网的建设奠定了政策基础。自然资源调查、时空信息服务、应急救灾服务等为天基物联网提供了广阔的应用空间,天基物联网已经具备了良好的建设基础。介绍了国内外天基物联网的发展现状,结合物联网的定义阐释了天基物联网的基本概念和体系结构,分析了天基物联网技术发展趋势,可为天基物联网的技术研究和建设应用提供参考。     

Community detection and service discovery on Social Internet of Things

Due to the increasing growth of objects and problems such as increased traffic, overload, delay in response, and low search volume in the service discovery process in the complex Social Internet of Things (SIoT) environment, we provide an effective mechanism in the service discovery process by grouping objects based on common criteria that help us improve service search performance. In this article, we present a new method for clustering objects so that we can group objects that have common services and can work together. Hence, we create a set of different associations for the type of service and reciprocal cooperation of objects. With its help, instead of a global network search, we can perform service searches locally more efficiently and ensure the accuracy and correctness of searches and their answers. Then, we have provided a new mechanism for the service discovery process. In addition, we categorized communities based on their size to compare our proposed algorithm with other approaches using factors such as modularity in SIoT. Finally, we achieved sufficient efficiency in service discovery (86.81% and 88.28%) and demonstrated better performance of the proposed approach in identifying communities.     

HoneyBow: 一个基于高交互式蜜罐技术的恶意代码自动捕获器

恶意代码已成为互联网最为严重的安全威胁之一，自动化捕获恶意代码样本是及时有效地应对恶意代码传播的必要前提，提出了一个基于高交互式蜜罐技术的恶意代码自动捕获器HoneyBow。相比较于基于低交互式蜜罐技术的Nepenthes恶意代码捕获器，HoneyBow具有恶意代码捕获类型更为全面、能够捕获未知恶意代码的优势，互联网上的实际恶意代码捕获记录对比和Mocbot蠕虫的应急响应处理实例对其进行了充分验证。     

基于微分博弈的在线社交网络恶意程序传播优化控制方法

针对在线社交网络（OSN）易传播恶意程序的现状,通过扩展传统的传染病理论,在考虑防御者和恶意程序主观努力度的基础上,提出了能确切描述OSN恶意程序的微分方程模型。利用微分博弈,建立了能反映防御者和恶意程序交互过程的OSN“恶意程序防御微分博弈”模型,当恶意程序动态改变其最优控制策略时,为防御者给出最优动态控制策略。实验结果表明,提出的方法能明显地抑制OSN恶意程序的传播,为防御OSN恶意程序提供了新途径。     

Modeling Propagation Dynamics of Bluetooth Worms (Extended Version)

In the last few years, the growing popularity of mobile devices has made them attractive to virus and worm writers. One communication channel often exploited by mobile malware is the Bluetooth interface. In this paper, we present a detailed analytical model that characterizes the propagation dynamics of Bluetooth worms. Our model captures not only the behavior of the Bluetooth protocol but also the impact of mobility patterns on the Bluetooth worm propagation. Validation experiments against a detailed discrete-event Bluetooth worm simulator reveal that our model predicts the propagation dynamics of Bluetooth worms with high accuracy. We further use our model to efficiently predict the propagation curve of Bluetooth worms in big cities such as Los Angeles. Our model not only sheds light on the propagation dynamics of Bluetooth worms, but also allows to predict spreading curves of Bluetooth worm propagation in large areas without the high computational cost of discrete-event simulation.     

ESOT: a new privacy model for preserving location privacy in Internet of Things

The Internet of Things (IoT) means connecting everything with every other thing through the Internet. In IoT, millions of devices communicate to exchange data and information with each other. During communication, security and privacy issues arise which need to be addressed. To protect information about users’ location, an efficient technique should be devised. Several techniques have already been proposed for preserving location privacy in IoT. However, the existing research lags in preserving location privacy in IoT and has highlighted several issues such as being specific or being restricted to a certain location. In this paper, we propose a new location privacy technique called the enhanced semantic obfuscation technique (ESOT) to preserve the location information of a user. Experimental results show that ESOT achieves improved location privacy and service utility when compared with a well-known existing approach, the semantic obfuscation technique.     

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.     

基于特征聚类的海量恶意代码在线自动分析模型

针对传统海量恶意代码分析方法中自动特征提取能力不足以及家族判定时效性差等问题,通过动静态方法对大量样本行为构成和代码片段分布规律的研究,提出了基于特征聚类的海量恶意代码在线自动分析模型,包括基于API行为和代码片段的特征空间构建方法、自动特征提取算法和基于LSH的近邻聚类算法。实验结果表明该模型具有大规模样本自动特征提取、支持在线数据聚类、家族判定准确率高等优势,依据该模型设计的原型系统实用性较强。     

基于博弈论的无线传感器网络恶意程序传播模型

恶意程序传播是无线传感器网络（wireless sensor network,WSN）面临的一类重要安全问题。从博弈论的角度对WSN恶意程序传播的微观机理进行分析,建立了WSN的攻防博弈模型,求出了博弈模型的混合纳什均衡解,并根据博弈双方的混合纳什均衡策略确定恶意程序的传染概率,从而建立了WSN的恶意程序传播模型。通过使用元胞自动机方法对WSN的恶意程序传播过程进行模拟,揭示了恶意程序的传播速度与博弈参数之间的关系,研究结果对抑制WSN恶意程序传播具有理论指导意义。     

A reputation-based RPL protocol to detect selective forwarding attack in Internet of Things

Internet of Things (IoT) is an internet of smart objects where smart objects communicate with each other. IoT objects are deployed in open medium with dynamic topology. Due lack of infrastructure and centralized management, IoT present serious vulnerabilities to security attacks. Therefore, security is an essential prerequisite for the real-world deployment of IoT. In this work, we propose reputation-based RPL protocol where reputation-based mechanism is embedded into RPL protocol to enhance its capabilities against selective forwarding attack. Reputation is calculated by evaluating data forwarding behavior of IoT node. Data forwarding behavior of IoT node is evaluated by the difference between monitored actual packet loss and estimated normal loss. Calculated reputation value is considered in parent selection. Simulation results show that the proposed approach can accurately detect and isolate selective forwarding attack with improving data delivery ratio of the IoT network.     

Incremental clustering method based on Gaussian mixture model to identify malware family

Aiming at the logical similarity of the behavioral characteristics of malware belonging to the same family,the characteristics of malware were extracted by tracking the logic rules of API function call from the perspective of behavior detection,and the static analysis and dynamic analysis methods were combined to analyze malicious behavior characteristics.In addition,according to the purpose,inheritance and diversity of the malware family,the transitive closure relationship of the malware family was constructed,and then the incremental clustering method based on Gaussian mixture model was improved to identify the malware family.Experiments show that the proposed method can not only save the storage space of malware detection,but also significantly improve the detection accuracy and recognition efficiency.     

基于改进贝叶斯分类的Android恶意软件检测

针对Android手机安全受恶意软件威胁越来越严重这一问题,提出一种改进的Android恶意软件检测算法。监控从Android移动设备应用程序获取的多种行为特征值,应用机器学习技术,通过与卡方检验滤波测试结合的方式改进传统的朴素贝叶斯算法,检测Android系统中的恶意软件。通过实验仿真,结果表明在采取朴素贝叶斯分类模型之前,使用卡方检验过滤应用程序的行为特征,可以使基于Android的恶意软件检测技术拥有较低的误报率和较高的精度。     

一种恶意软件实时检测模型

启发式扫描检测入侵行为未知的恶意软件,存在误报及漏报问题,且不能有效监控Rootkit。基于＂通过监控某种恶意行为,实现对一类入侵方式未知的恶意软件的实时检测＂的思想,提出了一种实时检测入侵行为未知恶意软件的Petri网模型,给出了性能测量及优化方法。通过在模型指导下建立的恶意软件实时检测系统中采集关键参数,完成了模型性能评价和调整。设计的系统可实时准确地检测具有特征行为的恶意软件。     

当前移动应用软件常用安全检测技术

在各类移动应用给人们的生活带来便利的同时,恶意应用对终端安全的威胁也在逐渐增多。文章针对恶意应用安全检测的问题,总结了四种常用的检测技术:静置检测、特征码扫描、二进制代码逆向分析和动态行为监测,给出了这四种技术的检测方法、检测流程以及关键技术,分析了每种技术的优点和不足。     

移动无线传感网中恶意软件传播的最优安全策略

移动无线传感器网络的大规模应用依赖于建立起应对恶意软件攻击的安全策略.一个有效的防护措施就是对传感器节点安装免疫补丁或清除节点中的病毒.考虑到传感器节点的移动特性,根据传染病学理论我们建立了恶意软件传播的动力学模型.基于此模型提出了以易感节点免疫比例与感染节点恢复比例作为优化控制变量的最优目标函数,使得在任意终止时刻被感染的节点数量最少并且实施安全措施成本最小.通过平衡点的稳定性分析,得到了恶意软件传播与否的阈值.运用庞德里亚金（Pontryagin）极大值原理得到了免疫比例与恢复比例的最优控制变量对.仿真结果表明,该模型对于建立遏制恶意程序在移动无线传感器网络中扩散传播的安全策略具有指导意义.     

基于物联网技术的智能医疗在健康检测仪中的应用

智能医疗行业在物联网技术的推动下发展迅速,智能医疗产品也逐渐进入到人们的生活中。基于物联网技术的智能医疗让病人和医疗设备以及医疗机构之间的联系更加紧密,文章主要介绍物联网技术对智能医疗的推动,研究智能健康检测仪的发展现状以及发展趋势,以达到改善医疗产品用户体验的目的。