使用主动标记建立攻击连接链的关联

网络攻击者通常在攻击最后目标前使用双向交互式连接一系列中间主机(跳板机)掩盖其真实攻击路径,并使用匿名技术隐藏真实身份。论文利用主动网络技术,通过向流经主动路由器的双向连接链流量进行主动标记,减少关联计算复杂性,快速、准确地建立攻击连接链的关联。在主动网络环境下设计了利用主动标记检测跳板机的原型系统,并在ANTS中实现。分析了amTrace原型系统的性能以及主动关联对数据包转发的影响。     

基于ZigBee无线传感器网络在智能家居中的应用研究

基于无线传感器网络技术组建的智能家居网络能够对家居环境进行很好地监控,组网方便,将嵌有无线传感器的家具、家电和其他日常用品同互联网相连,可实现远距离控制,提供舒适、方便和人性化的智能家居环境。     

Toward in vivo nanoscale communication networks: utilizing an active network architecture

A safe and reliable in vivo nanoscale communication network will be of great benefit for medical diagnosis and monitoring as well as medical implant communication. This review article provides a brief introduction to nanoscale and molecular networking in general and provides opinions on the role of active networking for in vivo nanoscale information transport. While there are many in vivo communication mechanisms that can be leveraged, for example, forms of cell signaling, gap junctions, calcium and ion signaling, and circulatory borne communication, this review examines two in particular: molecular motor transport and neuronal information communication. Molecular motors transport molecules representing information and neural coding operates by means of the action potential; these mechanisms are reviewed within the theoretical framework of an active network. This review suggests that an active networking paradigm is necessary at the nanoscale along with a new communication constraint, namely, minimizing the communication impact upon the living environment. The goal is to assemble efficient nanoscale and molecular communication channels while minimizing disruption to the host organism.     

Toward in vivo nanoscale communication networks: utilizing an active network architecture

A safe and reliable in vivo nanoscale communication network will be of great benefit for medical diagnosis and monitoring as well as medical implant communication. This review article provides a brief introduction to nanoscale and molecular networking in general and provides opinions on the role of active networking for in vivo nanoscale information transport. While there are many in vivo communication mechanisms that can be leveraged, for example, forms of cell signaling, gap junctions, calcium and ion signaling, and circulatory borne communication, this review examines two in particular: molecular motor transport and neuronal information communication. Molecular motors transport molecules representing information and neural coding operates by means of the action potential; these mechanisms are reviewed within the theoretical framework of an active network. This review suggests that an active networking paradigm is necessary at the nanoscale along with a new communication constraint, namely, minimizing the communication impact upon the living environment. The goal is to assemble efficient nanoscale and molecular communication channels while minimizing disruption to the host organism.     

An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique

The deployment of wireless sensor networks and mobile ad-hoc networks in applications such as emergency services, warfare and health monitoring poses the threat of various cyber hazards, intrusions and attacks as a consequence of these networks’ openness. Among the most significant research difficulties in such networks safety is intrusion detection, whose target is to distinguish between misuse and abnormal behavior so as to ensure secure, reliable network operations and services. Intrusion detection is best delivered by multi-agent system technologies and advanced computing techniques. To date, diverse soft computing and machine learning techniques in terms of computational intelligence have been utilized to create Intrusion Detection and Prevention Systems (IDPS), yet the literature does not report any state-of-the-art reviews investigating the performance and consequences of such techniques solving wireless environment intrusion recognition issues as they gain entry into cloud computing. The principal contribution of this paper is a review and categorization of existing IDPS schemes in terms of traditional artificial computational intelligence with a multi-agent support. The significance of the techniques and methodologies and their performance and limitations are additionally analyzed in this study, and the limitations are addressed as challenges to obtain a set of requirements for IDPS in establishing a collaborative-based wireless IDPS (Co-WIDPS) architectural design. It amalgamates a fuzzy reinforcement learning knowledge management by creating a far superior technological platform that is far more accurate in detecting attacks. In conclusion, we elaborate on several key future research topics with the potential to accelerate the progress and deployment of computational intelligence based Co-WIDPSs.     

基于QoS多播路由技术研究

随着Internet、移动网络和高性能网络的不断发展,在网络和不确定参数下具有QoS多播路由优化技术已成为网络及分布式系统领域的一个重要研究课题,这也是下一代Internet和高性能网络的难题。它吸引了许多爱好者。该文重点论述QoS多播路由需求,并简要阐明当前最新多目标优化问题的方法。定义了QoS多播路由作为多目标优化问题(即路由问题的网络模型)以及它是NP-复杂性问题。然后,重点讨论QoS多播路由的解决方法,包括约束多播树、多目标优化、计算智能和人工智能等。最后,文章建议一个约束多播路由结构和讨论一些将来的研究展望。     

A Survey of Application Layer Techniques for Adaptive Streaming of Multimedia

Though the integrated services model and resource reservation protocol (RSVP) provide support for quality of service, in the current Internet only best-effort traffic is widely supported. New high-speed technologies such as ATM (asynchronous transfer mode), gigabit Ethernet, fast Ethernet, and frame relay, have spurred higher user expectations. These technologies are expected to support real-time applications such as video-on-demand, Internet telephony, distance education and video-broadcasting. Towards this end, networking methods such as service classes and quality of service models are being developed. Today's Internet is a heterogeneous networking environment. In such an environment, resources available to multimedia applications vary. To adapt to the changes in network conditions, both networking techniques and application layer techniques have been proposed. In this paper, we focus on the application level techniques, including methods based on compression algorithm features, layered encoding, rate shaping, adaptive error control, and bandwidth smoothing. We also discuss operating system methods to support adaptive multimedia. Throughout the paper, we discuss how feedback from lower networking layers can be used by these application-level adaptation schemes to deliver the highest quality content.     

Employing computational intelligence to generate more intelligent and energy efficient living spaces

Our living environments are being gradually occupied with an abundant number of digital objects that have networking and computing capabilities. After these devices are plugged into a network, they initially advertise their presence and capabilities in the form of services so that they can be discovered and, if desired, exploited by the user or other networked devices. With the increasing number of these devices attached to the network, the complexity to configure and control them increases, which may lead to major processing and communication overhead. Hence, the devices are no longer expected to just act as primitive stand-alone appliances that only provide the facilities and services to the user they are designed for, but also offer complex services that emerge from unique combinations of devices. This creates the necessity for these devices to be equipped with some sort of intelligence and self-awareness to enable them to be self-configuring and self-programming. However, with this ＂smart evolution＂, the cognitive load to configure and control such spaces becomes immense. One way to relieve this load is by employing artificial intelligence （AI） techniques to create an intelligent ＂presence＂ where the system will be able to recognize the users and autonomously program the environment to be energy efficient and responsive to the user＇s needs and behaviours. These AI mechanisms should be embedded in the user＇s environments and should operate in a non-intrusive manner. This paper will show how computational intelligence （CI）, which is an emerging domain of AI, could be employed and embedded in our living spaces to help such environments to be more energy efficient, intelligent, adaptive and convenient to the users.     

主动式路由器操作系统TH-AOSR的设计与实现

传统网络存在着自身难以克服的弊端,如新的网络协议、新的用户服务在现行网络上实施、推广困难.而主动网络计算则是解决这一问题的一个可行方案.文章设计并实现了一个主动式路由器操作系统——TH-AOSR(Tsinghua active operating system for router),它在兼容传统路由器功能的同时,还具有主动网络互连和计算能力,可以方便地为用户或应用提供定制服务.     

Improving Slow-start based probing mechanisms for flow adaptation after handovers

In a heterogeneous mobile networking environment, when a mobile user needs to perform a handover and switch to a new network, besides all the mobility management procedures to keep its connections alive and divert its on-going flows to the new location, the transmission rate of these flows should also be correctly adapted to match the conditions of the new network. One of the common practices is to let the flows to go through a fresh new Slow-start (or its variants) phase to probe along the path between the new location of the mobile user and its correspondent for the appropriate rate. Unfortunately, in many situations packets from these flows will be leaked into the new path at the old rate already before the Slow-start probing is performed, which not only interferes with the probing process but also disturbs the new network and affects the cross traffic along the new path. In this paper we propose to introduce some intelligence into the network, more specifically onto the mobility agent, so that this packet leaking issue is well taken care of before and during the Slow-start probing. On the other hand, with the introduced intelligence the probing process can also be improved in the sense of promptness, speed and data throughput, and these improvements are clearly demonstrated by simulation results.     

主动网技术的发展趋势

主动网(ActiveNetwork)是一种新型的网络体系结构,与传统网络体系结构相比,其显著特点是,主动网增加了路由器或交换机等网络结点的计算能力,从而为用户提供了方便、灵活的网络服务定制能力。随着网络应用的日益增长和多样化,用户越来越要求网络能够提供灵活的、可扩展的网络服务。该文对主动网体系结构进行了深入的研究,结合当前国际上对主动网技术的研究新动向,重点讨论了主动网的服务定制能力,并提出了今后的发展方向。     

主动网络的研究进展与展望

主动网络是一种新型的网络体系结构,与传统网络体系结构相比,主动网络增加了路由器或交换机等网络中间节点的计算能力和可编程能力,从而为用户提供了方便、灵活的网络服务定制能力.随着网络应用的日益增长和多样化,用户要求网络能够提供灵活、可扩展的网络服务.该文全面总结了主动网络的研究现状,结合主动网络技术的研究新动向和主动网络的应用,给出了今后的研究方向.     

APPLIED COMPUTATIONAL INTELLIGENCE FOR FINANCE AND ECONOMICS

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.     

强干扰环境下网络情报数据滤波通信系统设计

传统通信系统在强干扰环境下进行通信时,系统会出现性能下降甚至无法通信的问题。设计并实现了一种新的网络情报数据滤波通信系统,设计强干扰环境下网络情报数据滤波通信系统的整体结构;按照网络情报数据滤波通信系统的标准以及分析强干扰的特性,对瞬态电压抑制模块、瞬态保护模块、语音信号输入模块进行设计,采用电磁滤波去噪的方法对故障修复模块进行优化,实现网络情报数据滤波通信系统的设计。实验表明,改进的网络情报数据滤波通信系统较传统的通信系统具有较强的抗干扰能力,通信效果好等明显优势。     

Reinforcement learning for context awareness and intelligence in wireless networks: Review, new features and open issues

In wireless networks, context awareness and intelligence are capabilities that enable each host to observe, learn, and respond to its complex and dynamic operating environment in an efficient manner. These capabilities contrast with traditional approaches where each host adheres to a predefined set of rules, and responds accordingly. In recent years, context awareness and intelligence have gained tremendous popularity due to the substantial network-wide performance enhancement they have to offer. In this article, we advocate the use of reinforcement learning (RL) to achieve context awareness and intelligence. The RL approach has been applied in a variety of schemes such as routing, resource management and dynamic channel selection in wireless networks. Examples of wireless networks are mobile ad hoc networks, wireless sensor networks, cellular networks and cognitive radio networks. This article presents an overview of classical RL and three extensions, including events, rules and agent interaction and coordination, to wireless networks. We discuss how several wireless network schemes have been approached using RL to provide network performance enhancement, and also open issues associated with this approach. Throughout the paper, discussions are presented in a tutorial manner, and are related to existing work in order to establish a foundation for further research in this field, specifically, for the improvement of the RL approach in the context of wireless networking, for the improvement of the RL approach through the use of the extensions in existing schemes, as well as for the design and implementation of RL in new schemes.     

面向开放移动网络环境的新一代中间件技术研究

结合现有网络计算环境的特点,对适用于开放、移动网络环境的新一代中间件技术进行了研究。在分析今天网络环境关键特征的基础上,提出了新一代中间件系统需要满足的技术需求;从所针对的问题角度对新一代中间件系统面临的主要挑战进行了详细的讨论,包括上下文感知、环境自适应、开放式协作、服务发现和普适互操作等,并给出了一些解决方案。     

计算智能研究综述

计算智能是人们在长期的研究过程中,受到自然界形形色色规律的启迪,根据这些规律的原理,模仿求解现实世界问题,它已经成为智能信息科学中最活跃的研究领域之一。针对计算智能几个相对独立的分支：神经网络、进化计算、群体智能和免疫系统,分析了其典型算法的原理、应用领域、优缺点和研究现状,指明了未来的研究方向。     

Analysis of computational intelligence techniques for diabetes mellitus prediction

Diabetes as a chronic disease is becoming a foremost community health concern worldwide. In developing countries, the diabetic patients are increasing rapidly due to lack of sentience and bad eating habits. So, there is a need of a framework that can effectively diagnose thousands of patients using clinical specifics. This work uses six computational intelligence techniques for diabetes mellitus prediction namely classification tree, support vector machine, logistic regression, naïve Bayes, and artificial neural network. The performance of these techniques was evaluated on eight different classification performance measurements. Moreover, these techniques were appraised on a receiver operative characteristic curve. Classification accuracy of 77 and 78% was achieved by artificial neural network and logistic regression, respectively, with F 1 measure of 0.83 and 0.84.

     

Digital image integrity – a survey of protection and verification techniques

We are currently on a verge of a revolution in digital photography. Developments in computational imaging and adoption of artificial intelligence have spawned new editing techniques that give impressive results in astonishingly short time-frames. The advent of multi-sensor and multi-lens cameras will further challenge many existing integrity verification techniques. As a result, it will be necessary to re-evaluate our notion of image authenticity and look for new techniques that could work efficiently in this new reality. The goal of this paper is to thoroughly review existing techniques for protection and verification of digital image integrity. In contrast to other recent surveys, the discussion covers the most important developments both in active protection and in passive forensic analysis techniques. Existing approaches are analyzed with respect to their capabilities, fundamental limitations, and prospective attack vectors. Whenever possible, the discussion is supplemented with real operation examples and a list of available implementations. Finally, the paper reviews resources available in the research community, including public data-sets and commercial or open-source software. The paper concludes by discussing relevant developments in computational imaging and highlighting future challenges and open research problems.     

Using computational intelligence to forecast carbon prices

European Union has introduced the European Trading System (ETS) as a tool for developing and implementing international treaties related to climate changes and to identify the most cost-effective methods for reducing greenhouse gas emissions, in particular carbon dioxide (CO₂), which is the most substantial. Companies producing carbon emissions must effectively manage associated costs by buying or selling carbon emission futures. Viewed from this perspective, this paper provides a model for managing the risk by buying and selling carbon emission futures by implementing techniques that leverage computational intelligence. Three computational intelligence techniques are proposed to provide accurate and timely forecasts for changes in the price of carbon: a novel hybrid neuro-fuzzy controller that forms a closed-loop feedback mechanism called PATSOS; an artificial neural network (ANN) based system; an adaptive neuro-fuzzy inference system (ANFIS). Results are based on 1074 daily carbon price observations collected to comprise a useful time-series dataset and for evaluation of the proposed techniques. The extra-sample performance of the proposed techniques is calculated. Analysis results are compared with those produced by other models. Comparison studies reveal that PATSOS is the most accurate and promising methodology for predicting the price of carbon. It is stated that this paper registers a first attempt to apply a hybrid neuro-fuzzy controller to forecasting carbon prices.