Architectures for the Future Networks and the Next Generation Internet: A Survey

Networking research funding agencies in the USA, Europe, Japan, and other countries are encouraging research on revolutionary networking architectures that may or may not be bound by the restrictions of the current TCP/IP based Internet. We present a comprehensive survey of such research projects and activities. The topics covered include various testbeds for experimentations for new architectures, new security mechanisms, content delivery mechanisms, management and control frameworks, service architectures, and routing mechanisms. Delay/Disruption tolerant networks, which allow communications even when complete end-to-end path is not available, are also discussed

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Statistical learning in network architecture

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.This electronic version was submitted by the student author. The certified thesis is available in the Institute Archives and Special Collections.Includes bibliographical references (p. 167-[177]).The Internet has become a ubiquitous substrate for communication in all parts of society. However, many original assumptions underlying its design are changing. Amid problems of scale, complexity, trust and security, the modern Internet accommodates increasingly critical services. Operators face a security arms race while balancing policy constraints, network demands and commercial relationships. This thesis espouses learning to embrace the Internet's inherent complexity, address diverse problems and provide a component of the network's continued evolution. Malicious nodes, cooperative competition and lack of instrumentation on the Internet imply an environment with partial information. Learning is thus an attractive and principled means to ensure generality and reconcile noisy, missing or conflicting data. We use learning to capitalize on under-utilized information and infer behavior more reliably, and on faster time-scales, than humans with only local perspective. Yet the intrinsic dynamic and distributed nature of networks presents interesting challenges to learning. In pursuit of viable solutions to several real-world Internet performance and security problems, we apply statistical learning methods as well as develop new, network-specific algorithms as a step toward overcoming these challenges. Throughout, we reconcile including intelligence at different points in the network with the end-to-end arguments. We first consider learning as an end-node optimization for efficient peer-to-peer overlay neighbor selection and agent-centric latency prediction. We then turn to security and use learning to exploit fundamental weaknesses in malicious traffic streams. Our method is both adaptable and not easily subvertible. Next, we show that certain security and optimization problems require collaboration, global scope and broad views.(cont.) We employ ensembles of weak classifiers within the network core to mitigate IP source address forgery attacks, thereby removing incentive and coordination issues surrounding existing practice. Finally, we argue for learning within the routing plane as a means to directly optimize and balance provider and user objectives. This thesis thus serves first to validate the potential for using learning methods to address several distinct problems on the Internet and second to illuminate design principles in building such intelligent systems in network architecture.by Robert Edward Beverly, IV.Ph.D

Context-Aware Recommendation Systems in Mobile Environments

Nowadays, the huge amount of information available may easily overwhelm users when they need to take a decision that involves choosing among several options. As a solution to this problem, Recommendation Systems (RS) have emerged to offer relevant items to users. The main goal of these systems is to recommend certain items based on user preferences. Unfortunately, traditional recommendation systems do not consider the user’s context as an important dimension to ensure high-quality recommendations. Motivated by the need to incorporate contextual information during the recommendation process, Context-Aware Recommendation Systems (CARS) have emerged. However, these recent recommendation systems are not designed with mobile users in mind, where the context and the movements of the users and items may be important factors to consider when deciding which items should be recommended. Therefore, context-aware recommendation models should be able to effectively and efficiently exploit the dynamic context of the mobile user in order to offer her/him suitable recommendations and keep them up-to-date.The research area of this thesis belongs to the fields of context-aware recommendation systems and mobile computing. We focus on the following scientific problem: how could we facilitate the development of context-aware recommendation systems in mobile environments to provide users with relevant recommendations? This work is motivated by the lack of generic and flexible context-aware recommendation frameworks that consider aspects related to mobile users and mobile computing. In order to solve the identified problem, we pursue the following general goal: the design and implementation of a context-aware recommendation framework for mobile computing environments that facilitates the development of context-aware recommendation applications for mobile users. In the thesis, we contribute to bridge the gap not only between recommendation systems and context-aware computing, but also between CARS and mobile computing.<br /

A Comprehensive Survey of Voice over IP Security Research

We present a comprehensive survey of Voice over IP security academic research, using a set of 245 publications forming a closed cross-citation set. We classify these papers according to an extended version of the VoIP Security Alliance (VoIPSA) Threat Taxonomy. Our goal is to provide a roadmap for researchers seeking to understand existing capabilities and to identify gaps in addressing the numerous threats and vulnerabilities present in VoIP systems. We discuss the implications of our findings with respect to vulnerabilities reported in a variety of VoIP products. We identify two specific problem areas (denial of service, and service abuse) as requiring significant more attention from the research community. We also find that the overwhelming majority of the surveyed work takes a black box view of VoIP systems that avoids examining their internal structure and implementation. Such an approach may miss the mark in terms of addressing the main sources of vulnerabilities, i.e., implementation bugs and misconfigurations. Finally, we argue for further work on understanding cross-protocol and cross-mechanism vulnerabilities (emergent properties), which are the byproduct of a highly complex system-of-systems and an indication of the issues in future large-scale systems

Social Computing: An Overview

A collection of technologies termed social computing is driving a dramatic evolution of the Web, matching the dot-com era in growth, excitement, and investment. All of these share high degree of community formation, user level content creation, and computing, and a variety of other characteristics. We provide an overview of social computing and identify salient characteristics. We argue that social computing holds tremendous disruptive potential in the business world and can significantly impact society, and outline possible changes in organized human action that could be brought about. Social computing can also have deleterious effects associated with it, including security issues. We suggest that social computing should be a priority for researchers and business leaders and illustrate the fundamental shifts in communication, computing, collaboration, and commerce brought about by this trend

Examination of traditional botnet detection on Iot-based bots

A botnet is a collection of Internet-connected computers that have been suborned and are controlled externally for malicious purposes. Concomitant with the growth of the Internet of Things (IoT), botnets have been expanding to use IoT devices as their attack vectors. IoT devices utilise specific protocols and network topologies distinct from conventional computers that may render detection techniques ineffective on compromised IoT devices. This paper describes experiments involving the acquisition of several traditional botnet detection techniques, BotMiner, BotProbe, and BotHunter, to evaluate their capabilities when applied to IoT-based botnets. Multiple simulation environments, using internally developed network traffic generation software, were created to test these techniques on traditional and IoT-based networks, with multiple scenarios differentiated by the total number of hosts, the total number of infected hosts, the botnet command and control (CnC) type, and the presence of aberrant activity. Externally acquired datasets were also used to further test and validate the capabilities of each botnet detection technique. The results indicated, contrary to expectations, that BotMiner and BotProbe were able to detect IoT-based botnets—though they exhibited certain limitations specific to their operation. The results show that traditional botnet detection techniques are capable of detecting IoT-based botnets and that the different techniques may offer capabilities that complement one another

Entropy-Based Dynamic Ad Placement Algorithms for In-Video Advertising

With the evolution of the Internet and the increasing number of users over last years, online advertising has become one of the pillars models that sustains many of the Internet businesses. In this dissertation, we review the history of online advertising, will be made, as well as the state-of-the-art of the major scientific contributions in online advertising,in particularly in respect to in-video advertising. In in-video advertising, one of the major issues is to identify the best places for insertion of ads. In the literature, this problem is addressed in different ways. Some methods are designed for a specific genres of video, e.g., football or tennis, while others are independent of genre, trying to identify the meaningful video scenes (a set of continuous and related frames) where ads will be displayed. However, the vast majority of online videos in the Internet are not long enough to identify large scenes. So, in this dissertation we will address a new solution for advertisement insertion in online videos, a solution that can be utilized independently of the duration and genre of the video in question. When developing a solution for in-video advertising, a major challenge rests on the intrusiveness that the ad inserted will take upon the viewer. The intrusiveness is related to the place and timing used by the advertising to be inserted. For these reasons, the algorithm has to take in consideration the "where", "when" and "how" the advertisement should be inserted in the video, so that it is possible to reduce the intrusiveness of the ads to the viewer. In short, in addition to besides being independent of duration and genre, the proposed method for ad placement in video was developed taking in consideration the ad intrusiveness to the user.Com a evolução da Internet e o número crescente de utilizadores ao longo destes últimos anos, a publicidade on-line tornou-se um dos modelos base que tem sustentado muitos negócios na Internet. Da mesma forma, vídeos on-line constituem uma parte significativa do tráfego na Internet. É por isso possível entender desta forma, o potencial que ferramentas que possão explorar eficientemente ambas estas áreas possuem no mercado. Nesta dissertação será feita uma revisão da história da publicidade online, mas também será apresentado ao leitor uma revisão sobre o estado da arte das principais contribuições científicas para a publicidade on-line, em especial para a publicidade em video. Na publicidade em vídeo, uma das principais preocupações é identificar os melhores locais para a inserir os anúncios. Na literatura, este problema é abordado de diferentes maneiras, alguns criaram métodos para gêneros específicos de vídeo, por exemplo, futebol ou ténis, outros métodos são independentes do gênero, mas tentam identificar as cenas de vídeo (um conjunto contínuo de frames relacionadas) e apenas exibir anúncios neles. No entanto, a grande maioria dos vídeos on-line na Internet não são suficiente longos para serem identificadas cenas suficientemente longas para inserir os anúncios. Assim, nesta dissertação iremos abordar uma nova solução para a inserção de anúnicios em vídeos, uma solução que pode ser utilizada de forma independente da duração e gênero do vídeo em questão. Ao desenvolver uma solução para inserir anúncos em vídeos a grande preocupação recai sobre a intromissão que o anúncio inserido poderá ter sobre o utilizador. A intrusão está relacionada com o local e tempo utilizado pela publicidade quando é inserida. Por estas razões, o algoritmo tem que levar em consideração "onde", "quando" e "como" o anúncio deve ser inserido no vídeo, de modo que seja possível reduzir a intromissão dos anúncios para o utilizador. Em suma, para além de ser independente da duração e gênero do vídeo, o método proposto será também desenvolvido tendo em consideração a intromissáo do anúncio para o utilizador. Por fim, o método proposto será testado e comparado com outros métodos, de modo a que seja possivel perceber as suas capacidades