Traditional media is delivered using segmented video, with variable bitrates, over protocols such as MPEG-DASH. This works well for media experiences with a single, or very few, pre-produced variants. When the number of possible variants increases, however, it results in an explosion of pre-produced whole-experience videos in a one-per-variant relationship; this in turn causes high storage costs and poor re-use of otherwise separable media assets. The paradigm of object-based media (OBM) offers a solution by keeping media entities distinct after production, allowing them to be combined flexibly at the point of consumption. We examine the delivery and playback of OBM using a novel WebAssembly-based media player running in the browser. This approach allows the selective render offload of parts of an experience into the edge/cloud, by migrating associated code from the browser. Using three diverse exemplars of OBM we present a common meta-data format to capture flexible experiences, and measure the performance of our delivery pipeline in a range of on-device and offloaded scenarios. As far as we are aware this is the first such study of generalised OBM media delivery.

Quality of Experience (QoE) is a key component in adaptive bitrate (ABR) streaming. Whilst the effects of delivery disruptions—such as changes in video quality or rebuffering—have been extensively studied, the impact of playback rate variations remains relatively unexplored. Existing work has examined the QoE impact of playback rate in isolation, without comparing it to other common ABR streaming artifacts such as video quality variations, rebuffering, or seeking. Moreover, the role of gradual playback rate transitions has not been explored. This article addresses these gaps through four large-scale subjective studies that provide a systematic QoE evaluation of playback rate. We compare the acceptability of playback rate changes against quality degradations and rebuffering, and assess rate increases relative to seeking, both of which are strategies for maintaining the desired latency in low-latency streaming. We further investigate how gradual versus instantaneous playback rate transitions affect QoE. Through our subjective studies, we identify levels of slowed-down playback that are imperceptible to users and can be combined with video quality adaptation in ABR algorithms to reduce rebuffering. Moreover, we identify imperceptible rates of speeded-up video playback, which can be used as part of catch-up mechanisms to maintain a desired latency in live streaming, offering a less detrimental alternative to seeking events, which we found to significantly degrade QoE. This work presents the first systematic comparison of playback rate variations with video quality degradation, rebuffering and seeking. The findings extend our understanding of QoE in adaptive video streaming and provide actionable design guidelines for video players to improve the user experience of streaming services.

Intent-based networking (IBN) has emerged as a promising paradigm to simplify the management of network infrastructures. At the heart of IBN systems lies the intent handler, a management entity that transforms declarative network goals into imperative network configuration that meets specific policy and optimization requirements. Practical research has produced a number of intent handler implementations, that exhibit specialized use-cases with limited extensibility. This paper presents SWIFT, a framework to simplify the development of custom intent scenarios. To support this capability, SWIFT delivers two main contributions. Firstly, it employs an ontology to abstract several standardized and open network data models (3GPP Core Configuration, ETSI NFV-MANO, ONOS NBI) and support the translation of slice delivery intents, modelled using the TMForum common intent model, into effective infrastructure configurations. Secondly, SWIFT offers a reasoning framework for network operators to implement custom intent handling logic using logical programming. The reasoner can use the rich information of the ontology both to automate the translation of intent requirements, and validate intent goals based on the current network state. Our proof-of-concept SWIFT implementation can automate and optimize the delivery of private 5G network slices. Furthermore, we demonstrate the optimal planning and delivery of mobile slice intents in a real mobile network topology with minimal processing overheads.

Driven by the need to better reflect and understand audience quality of experience content providers and deliverers are no longer solely using models for the Mean Opinion Score but also the opinion score distribution. In tandem, motivated by the desire to understand how these models predict the QoE and which features contribute positively to it, there has been increased emphasis on explainable QoE modelling. Recently the advantages of directly explainable models - where model outputs can be explained using the inputs and the model’s inner workings-have been championed. These models provide clear insights into how different features contribute positively, or negatively, to QoE. To date, research into directly explainable methods has focussed on MOS, rather than opinion score distribution, modelling. To bridge this gap we discuss the feasibility of using multinomial logistic regression for directly explainable MOS modelling, demonstrating our approach on a short form streamed video dataset and showing that it compares favourably to other explainable methods.

User-driven experience customisation can potentially enhance the Quality of Experience (QoE) in personalised multimedia. Such experiences could be, for example, delivered using HTTP Adaptive Streaming - the most prominent way of consuming media over the Internet. In this paper, we present a subjective study designed to investigate the QoE impact of user-driven customisation in personalised media experiences. We offered participants the option to customise their video layout, after which we asked them to score a range of quality impairments found in HTTP Adaptive Streaming. Based on our analysis of the collected Mean Opinion Scores (MOS), we found that experience customisation impacts the QoE in a surprising way. We found that experience personalisation caused participants’ expectations to increase when it comes to QoE, as they perceived the most severe quality impairments worse than the control. This establishes the need for specialised QoE models that take into account different levels of user expectations.

Intent-based networking (IBN) has emerged as a promising paradigm to improve management automation in network infrastructures. At the heart of IBN systems lies the intent handler, an intelligent translation process that transforms declarative network goals into imperative network configurations. Although IBN research has demonstrated the ability to support complex automation scenarios, existing intent handlers have limited scope, targeting specialized use cases and optimizations, and offering limited extensibility. This paper presents Oz, an intent framework baed on Semantic Web technologies. Oz uses an ontology to combine several standardized data models and support the translation of TMForum common intent model expressions into effective infrastructure configurations. Furthermore, Oz implements a semantic web reasoner that allows operators to realize intents using logic rules that interpret intent requirements into configuration and validate intent fulfillment based on the current network state. Using a production network topology, we demonstrate that Oz can plan the deployment of a cache service intent on a busy network in less than 10 seconds, by using a contextual state exploration intent implementation.

The surge in popularity of live video streaming has spurred the development of various bitrate adaptation techniques, all aimed at enhancing user Quality of Experience (QoE). Compared to streaming Video-on-Demand, achieving low-latency live video streaming under fluctuating network conditions poses additional challenges. It requires finding the balance between rebuffering avoidance and latency, as a small client buffer is required to achieve low latency. Video players can also employ playback speed control to help optimize this balance. Specifically, when client buffer occupancy is high and hence latency is high, the player may increase playback speed to reduce the latency; and conversely, when client buffer occupancy is low and hence the risk of rebuffering is high, the player may reduce playback speed to increase buffer occupancy. Based on this rationale, a variety of playback speed control methods have been proposed. This paper evaluates, using a real-world testbed, the effectiveness of various playback speed control mechanisms when applied to a set of bitrate adaptation algorithms, with the evaluation also encompassing variations in target latency and network conditions. Our findings show a lack of coordination between adaptive bitrate (ABR) algorithms and playback speed control mechanisms. This leads us to conclude that there is a need for new playback speed control methods designed in conjunction with ABR algorithms.

Quality of Experience (QoE) is a crucial component of adaptive bitrate (ABR) streaming, with the effects of abrupt changes in playback quality or rebuffering, caused by delivery disruptions, being widely studied. However, the collective ABR community has a limited understanding of the effects of changes in playback rate on QoE. In this pioneering work, we investigate two aspects of playback rate fluctuations. In particular, we carry out two subjective studies to assess if a change in playback rate is more or less acceptable than a drop in video quality or a rebuffering event. Furthermore, we examine the effect of the transition in playback rate on QoE, comparing gradual and instant variations. Our subjective studies recruited 120 participants who evaluated 102 test sequences. In summary, we find that playback rate drops of 0.8-0.9 are imperceptible for most content, and rated similarly to a video quality drop to medium level. In contrast, lower playback rates of 0.6-0.7 were perceived as poorly as rebuffering events. Gradual changes in playback rate can offer better QoE, but only in limited cases depending on the content, target playback rate, as well as magnitude of change.

Abstract HTTP Adaptive Streaming (HAS), the most prominent technology for streaming video over the Internet, suffers from high end-to-end latency when compared to conventional broadcast methods. This latency is caused by the content being delivered as segments rather than as a continuous stream, requiring the client to buffer significant amounts of data to provide resilience to variations in network throughput and enable continuous playout of content without stalling. The client uses an Adaptive Bitrate (ABR) algorithm to select the quality at which to request each segment to trade-off video quality with the avoidance of stalling to improve the Quality of Experience (QoE). The speed at which the ABR algorithm responds to changes in network conditions influences the amount of data that needs to be buffered, and hence to achieve low latency the ABR needs to respond quickly. Llama (Lyko et al. 28) is a new low latency ABR algorithm that we have previously proposed and assessed against four on-demand ABR algorithms. In this article, we report an evaluation of Llama that demonstrates its suitability for low latency streaming and compares its performance against three state-of-the-art low latency ABR algorithms across multiple QoE metrics and in various network scenarios. Additionally, we report an extensive subjective test to assess the impact of variations in video quality on QoE, where the variations are derived from ABR behaviour observed in the evaluation, using short segments and scenarios. We publish our subjective testing results in full and make our throughput traces available to the research community.

Industry 4.0 and the trend of connecting legacy Industrial Control Systems (ICSs) to public networks have exposed these systems to various online threats. To combat these threats, honeypots have been widely used to provide proactive monitoring, detection and deception security capabilities. However, skilled attackers are now adept at fingerprinting and avoiding honeypots. Therefore, we take a fundamentally different approach in this paper. Instead of the honeypot representing a real system, we deploy it as a deterrent. Through obfuscation, the aim is to make an attacker believe the real system is a honeypot and collect threat intelligence data on the attacker. To achieve this, we introduce a new obfuscation technique that allows real ICSs to present themselves as honeypots. By taking advantage of honeypot fingerprinting techniques, we are able to deter attackers from interacting with the real Programmable Logic Controller (PLC) within the industrial network. The approach is implemented and evaluated using different penetration testing tools and an expert evaluation highlighting the benefits of obfuscation in that potential adversaries would be misled into assuming the PLC is a honeypot.

Network softwarization has revitalized the interest of the network community towards emulation as an effective mechanism for network experimentation. Relevant platforms automate the deployment of virtual network topologies on a host, providing users the ability to manually run experimental scenarios. Whilst this may suit prototyping, modern development and deployment practices such as CI/CD depend on fully automated testing processes, built around high-level testing APIs and abstracting the challenges involved with synchronizing complex node interaction scenarios. In this paper, we present Network Emulation System (NES): a cloud-native, and highly-parallelizable Network Emulation as a Service (NEaaS) platform designed from the ground up to facilitate codeless experiment specification and to automate network testing workflows in cloud CI/CD environments. We demonstrate that NES offers a 8x speedup improvement in topology instantiation times in comparison to existing emulation platforms, and its life-cycle model can automate testing processes for complex service configurations using existing CI/CD platforms such as GitHub Actions.

Video streaming continues to be the largest service delivered on the internet. This includes gaming videos, delivered both on-demand and live, where gaming footage is usually accompanied by a video of the player overlaid on top of the gameplay - resulting in Picture-In-Picture (PiP) content. Currently, PiP content is usually combined into a single video before being delivered to the client via technologies such as HTTP Adaptive Streaming (HAS). In this study, we investigated the QoE importance of gameplay and player elements in PiP gaming videos by varying the video quality of these elements individually. We conducted a subjective study, testing nine quality permutations based on three quality levels across three pieces of content from different gaming genres, with 30 participants recruited using an ethical crowdsourcing platform. We found that gameplay was significantly more important in terms of overall QoE, while the player element made a difference in only a few cases.

Intent-based networking (IBN) systems have become the de-facto control abstraction to drive self-service, self-healing, and self-optimized capabilities in service delivery processes. Nonetheless, the operation complexity of modern network infrastructures make network practitioners apprehensive towards adoption in production, requiring further evidence for correctness. In this paper, we argue that testing, verification and monitoring should become first-class citizens in reference IBN architecture, in order to improve the detection errors during operations. Towards this goal, we present an extension for an intent architecture that allows IBN system to validate the correctness of network configuration using realistic network emulation. Furthermore, we present an intent use-case that ensure correct operation in hybrid networks.

The increasing softwarization of network infrastructures introduces an important challenge for network configuration. The growth of the network configuration space as a result of new device types and the expanding inter-dependence of network service components increases the network configuration complexity. Compounding this issue, new service deployment architectures lack mechanisms to validate the impact of service configuration on network resilience. Network operators need to adopt new mechanisms to validate and verify network configuration changes, taking inspiration from popular Continuous Integration/Continuous Development (CI/CD) mechanisms. This paper introduces Network Emulation-based Automated Testing (NEAT), an automated testing framework for network configuration. NEAT allows network managers to define network topologies and tests through YAML files, to then be later executed by NEAT within realistic network topologies. Furthermore, network managers can control the fidelity of their network tests and bound the execution time of testing suites, as well as exploit parallelization of modern servers to speedup test execution.

We propose the AQUILA framework (Adaptive Quality Assurance in Industrial Applications), a concept for digitalization in Industry 4.0 to support the entire industrial manufacturing chain, laying the groundwork for adaptive quality assurance in times of disrupted supply chains and, due to the COVID-19 pandemic, restricted travel possibilities. To that end, our proposed framework allows for the definition and description of industrial processes, quality assurance and testing protocols, and training scenarios in a comprehensive notation based on BPMN, and supports users in task execution, documentation, and evaluation by providing smart glass-based HCI with eye tracking technology, delivering a combination of process documentation, context-sensitive AR visualization, gaze-based interaction schemes, and remote maintenance and assistance functionality.

Recent multimedia experiences using techniques such as DASH allow the streaming delivery to be adapted to suit network context. Object Based Media (OBM) provides even more flexibility as distinct media objects are streamed and combined based on user preferences, allowing the experience to be personalised for the user. As adaptation can lead to degradation, modelling and measuring Quality of Experience (QoE) are crucial to ensure a perceptibly-optimal user experience. QoE models proposed for DASH include quality-related factors from single video-object streams and hence, are unsuitable for multi-video OBM experiences. In this paper, we propose an objective method to quantify QoE for video-based OBM experiences. Our model provides different strategies to aggregate individual object QoE contributions for different OBM experience genres. We apply our model to a case study and contrast it with the QoE levels obtained using a standard QoE model for DASH.

The advent of Industry 4.0 and smart manufacturing has led to an increased convergence of traditional manufacturing and production technologies with IP communications. Legacy Industrial Control System (ICS) devices, now interconnected via public networks, are exposed to a wide range of previously unconsidered threats, which must be considered to ensure the continued safe operation of industrial processes. This paper surveys the ICS honeypot deployments in the literature to date, provides an overview of ICS focused threat vectors, and studies how honeypots can be integrated within an organisations defensive strategy. We discuss relevant legislation, such as the UK Cyber Assessment Framework, the US NIST Framework for Improving Critical Infrastructure Cybersecurity, and associated industry-based standards and guidelines supporting operator compliance. This is used to frame a discussion on our survey of existing ICS honeypot implementations, and the role of honeypots in supporting regulatory objectives. We observe that many low-interaction honeypots are limited in their use. This is largely due to the increased knowledge attackers have on how real-world ICS devices are configured and operate vs the configurability of simulated honeypot systems. Furthermore, we find that environments with increased interaction provide more extensive capabilities and value, due to their inherent obfuscation delivered through the use of real-world systems. Based on these insights, we propose a novel framework towards the classification and implementation of ICS honeypots.

The Internet of Things (IoT), in combination with advancements in Big Data, communications and networked systems, offers a positive impact across a range of sectors including health, energy, manufacturing and transport. By virtue of current business models adopted by manufacturers and ICT operators, IoT devices are deployed over various networked infrastructures with minimal security, opening up a range of new attack vectors. Conventional rule-based intrusion detection mechanisms used by network management solutions rely on pre-defined attack signatures and hence are unable to identify new attacks. In parallel, anomaly detection solutions tend to suffer from high false positive rates due to the limited statistical validation of ground truth data, which is used for profiling normal network behaviour. In this work we go beyond current solutions and leverage the coupling of anomaly detection and Cyber Threat Intelligence (CTI) with parallel processing for the profiling and detection of emerging cyber attacks. We demonstrate the design, implementation, and evaluation ofCitrus: a novel intrusion detection framework which is adept at tackling emerging threats through the collection and labelling of live attack data by utilising diverse Internet vantage points in order to detect and classify malicious behaviour using graph-based metrics as well as a range of machine learning (ML) algorithms. Citrus considers the importance of ground truth data validation and its flexible software architecture enables both the real-time and offline profiling, detection and classification of emerging cyber-attacks under optimal computational costs. Thus, establishing it as a viable and practical solution for next generation network defence and resilience strategies.

Intent-based networking provides an efficient mechanism to manage complexity in network management. The paradigm allows users to express their network requirements, and an autonomic framework translates them into a network configuration. Existing efforts focus primarily on modeling connectivity intents for end-users. Nonetheless, in order to deliver autonomic behavior in network management, an intent system must support a wider range of network management processes and model human-to-human interactions, essential for network operation. Furthermore, such interactions may involve nontechnical users and require the design of novel interfaces, supporting free-text and conversational intent expression. Towards this goal, we present an intent architecture that supports novel network management intents, such as network path rerouting and applying periods of ’service protection’. The paper includes details of our prototype implementation that is capable of deploying such intents in under five seconds in a large mininet topology.

The Intent-Based Northbound Interface (NBI) offers users the ability to express what they want to achieve instead of how to achieve it, enabling improvements to network management and reducing operational costs. However, development of an Intent-Based NBI remains in its infancy. Existing solutions do not allow users to express high-level operational targets that appropriately capture business objectives, nor link these to lower-level management policies and operations. We propose an extensible Intent-based NBI framework and a higher-level declarative intent expression to enable service-oriented intents with different targets. We focus on the creation of intents and their mapping from high-level expressions to low-level policies, and consider this from the perspective of an intent developer in the context of a Cloud CDN use case.

In the recent years, HTTP Adaptive Bit Rate (ABR) streaming including Dynamic Adaptive Streaming over HTTP (DASH) has become the most popular technology for video streaming over the Internet. The client device requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces high latency compared to traditional broadcast methods, mostly in the client buffer which needs to hold enough data to absorb any changes in network conditions. Clients employ an ABR algorithm which monitors network conditions and adjusts the quality at which segments are requested to maximise the user's Quality of Experience. The size of the client buffer depends on the ABR algorithm's capability to respond to changes in network conditions in a timely manner, hence, low latency live streaming requires an ABR algorithm that can perform well with a small client buffer. In this paper, we present Llama - a new ABR algorithm specifically designed to operate in such scenarios. Our new ABR algorithm employs the novel idea of using two independent throughput measurements made over different timescales. We have evaluated Llama by comparing it against four popular ABR algorithms in terms of multiple QoE metrics, across multiple client settings, and in various network scenarios based on CDN logs of a commercial live TV service. Llama outperforms other ABR algorithms, improving the P.1203 Mean Opinion Score (MOS) as well as reducing rebuffering by 33% when using DASH, and 68% with CMAF in the lowest latency scenario.

BGP has long been the de-facto control plane protocol for inter-network connectivity. Although initially designed to provide best-effort routing between ASes, the evolution of Internet services has created a demand for more complex control functionalities using the protocol. At the heart of this challenge lies the static nature of configuration mechanisms and the limited programmability of existing BGP speakers. Meanwhile, the SDN paradigm has demonstrated that open and generic network control APIs can greatly improve network functionality and seamlessly enable greater flexibility in network management. In this paper, we argue that BGP speaking systems can and should provide an open and rich control and configuration mechanism, in order to address modern era network control requirements. Towards this goal, we present λBGP, a modular and extensible BGP framework written in Haskell. The framework offers an extensible integration model for reactive BGP control that remains backward compatible with existing BGP standards and allows network managers to define route processing policies using a high-level language and to dynamically inject information sources into the path selection logic. Using a high-performance BGP traffic generator, we demonstrate that λBGP offers performance comparable to production BGP speakers, while dynamic AS route processing policies can be written in just a few lines of code.

Despite the Internet being an apex of human achievement for many years, malicious activity and cyber attacks are becoming more prevalent than ever before. Large scale data collection using threat sources such as honeypots have recently been employed to gather information relating to these attacks. While this data naturally details attack properties, there exists challenges in extracting the relevant information from vast data sets to provide valuable insight and a standard description of the attack. Traditionally, threats are identified through the use of signatures that are crafted manually through the composition of IOCs (Indicators of Compromise) extracted from telemetry captured during an attack process, which is often administered by an experienced engineer. These signatures have been proven effective in their use by IDSs (Intrusion Detection Systems) to detect emerging threats. However, little research has been made in automating the extraction of emerging IOCs and the generation of corresponding signatures which incorporate host artefacts. In this paper we present Citrus: a novel approach to the generation of signatures by incorporating host based telemetry extracted from honeypot endpoints. Leveraging this visibility at an endpoint grants a detailed understanding of bleeding edge attack tactics, techniques, and procedures gathered from host logs.

HTTP Adaptive Streaming (HAS) technologies such as MPEG DASH are now used extensively to deliver television services to large numbers of viewers. In HAS, the client requests segments of content using HTTP, with an ABR algorithm selecting the quality at which to request each segment to trade-off video quality with the avoidance of stalling. This introduces significant end to end latency compared to traditional broadcast, due to the the client requiring a large enough buffer for the ABR algorithm to react to changes in network conditions in a timely manner. The recently standardised Common Media Application Format (CMAF) has helped address the issue of latency by defining segments as composed of independently transferable chunks. In this paper, we describe a simulation model we have developed to evaluate the performance of four popular ABR algorithms using DASH and CMAF in various low latency live streaming scenarios. Realistic network conditions are used for the evaluation, which are based on throughput data taken from the CDN logs of a commercial live TV service. We quantify the performance of the ABR algorithms using a selection of QoE metrics, and show that CMAF can significantly improve ABR performance in low delay scenarios.

When designing Software Defined Networks (SDNs), there is a risk that the additional abstractions available can result in reduced scalability and performance. One such abstraction, intents, are a way in which network administrators can express policies rather than having to define specific forwarding rules. This provides a benefit to administrators in allowing automatic network reconfiguration and fault tolerance. In this paper, we highlight the performance overheads associated with the intents framework from a popular SDN controller, ONOS. We propose a novel prototype that leverages source-based routing and programmable data planes using P4 in order to reduce the overheads of intent-based forwarding.

Despite the relative maturity of the Internet, the computer networks of today are still susceptible to attack. The necessary distributed nature of networks for wide area connectivity has traditionally led to high cost and complexity in designing and implementing secure networks. With the introduction of software-defined networks (SDNs) and network functions virtualization, there are opportunities for efficient network threat detection and protection. SDN's global view provides a means of monitoring and defense across the entire network. However, current SDN-based security systems are limited by a centralized framework that introduces significant control plane overhead, leading to the saturation of vital control links. In this paper, we introduce TENNISON, a novel distributed SDN security framework that combines the efficiency of SDN control and monitoring with the resilience and scalability of a distributed system. TENNISON offers effective and proportionate monitoring and remediation, compatibility with widely available networking hardware, support for legacy networks, and a modular and extensible distributed design. We demonstrate the effectiveness and capabilities of the TENNISON framework through the use of four attack scenarios. These highlight multiple levels of monitoring, rapid detection, and remediation, and provide a unique insight into the impact of multiple controllers on network attack detection at scale.

The emerging network paradigm of Software Defined Networking (SDN) has been increasingly adopted to improve the Quality of Experiences (QoE) across multiple HTTP adaptive streaming (HAS) instances. However, there is currently a gap between research and reality in this field. QoE models, which offer user-level context to network management processes, are often tested in a simulation environment. Such environments do not consider the effects that network protocols, client programs, and other real world factors may have on the outcomes. Ultimately, this can lead to models not functioning as expected in real networks. On the other hand, setting up an experiment that reflects reality is a time consuming process requiring expert knowledge. This paper shares designs and guidelines of an SDN experimentation framework (SDQ), which offers rapid evaluation of QoE models using real network infrastructures.

The growing popularity and increasing performance of mobile devices is transforming the way in which media can be consumed, from single device playback to orchestrated multi-stream experiences across multiple devices. One of the biggest challenges in realizing such immersive media experience is the dynamic management of synchronicity between associated media streams. This is further complicated by the faceted aspects of user perception and heterogeneity of user devices and networks. This paper introduces a QoE-aware open inter-stream media synchronization framework (IMSync). IMSync employs efficient monitoring and control mechanisms, as well as a bespoke QoE impact model derived from subjective user experiments. Given a current lag, IMSync's aim is to use the impact model to determine a good catch-up strategy that minimizes detrimental impact on QoE. The impact model balances the accumulative impact of re-synchronization processes and the degree of non-synchronicity to ensure the QoE. Experimental results verify the run-time performance of the framework as a foundation for immersive media experience in open N-Screens cloud.

The Internet is often thought to be a model of resilience, due to a decentralised, organically-grown architecture. This paper puts this perception into perspective through the results of a security analysis of the Border Gateway Protocol (BGP) routing infrastructure. BGP is a fundamental Internet protocol and its intrinsic fragilities have been highlighted extensively in the literature. A seldom studied aspect is how robust the BGP infrastructure actually is as a result of nearly three decades of perpetual growth. Although global black-outs seem unlikely, local security events raise growing concerns on the robustness of the backbone. In order to better protect this critical infrastructure, it is crucial to understand its topology in the context of the weaknesses of BGP and to identify possible security scenarios. Firstly, we establish a comprehensive threat model that classifies main attack vectors, including but non limited to BGP vulnerabilities. We then construct maps of the European BGP backbone based on publicly available routing data. We analyse the topology of the backbone and establish several disruption scenarios that highlight the possible consequences of different types of attacks, for different attack capabilities. We also discuss existing mitigation and recovery strategies, and we propose improvements to enhance the robustness and resilience of the backbone. To our knowledge, this study is the first to combine a comprehensive threat analysis of BGP infrastructures withadvanced network topology considerations. We find that the BGP infrastructure is at higher risk than already understood, due to topologies that remain vulnerable to certain targeted attacks as a result of organic deployment over the years. Significant parts of the system are still uncharted territory, which warrants further investigation in this direction.

The growing demand for online distribution of high quality and high throughput content is dominating today's Internet infrastructure. This includes both production and user-generated media. Among the myriad of media distribution mechanisms, HTTP adaptive streaming (HAS) is becoming a popular choice for multi-screen and multi-bitrate media services over heterogeneous networks. HAS applications often compete for network resources without any coordination between each other. This leads to quality of experience (QoE) fluctuations on delivered content, and unfairness between end users, while new network protocols, technologies, and architectures, such as software defined networking (SDN), are being developed for the future Internet. The programmability, flexibility, and openness of these emerging developments can greatly assist the distribution of video over the Internet. This is driven by the increasing consumer demands and QoE requirements. This paper introduces a novel user-level fairness model UFair and its hierarchical variant UFairHA, which orchestrate HAS media streams using emerging network architectures and incorporate three fairness metrics (video quality, switching impact, and cost efficiency) to achieve user-level fairness in video distribution. UFairHAhas also been implemented in a purpose-built SDN testbed using open technologies, including OpenFlow. Experimental results demonstrate the performance and feasibility of our design for video distribution over future networks.

The past two decades have seen a shift in the multimedia consumption behaviours from that of collectivism and passivity, to individualism and activity. This paper introduces the architectural design, implementation and user evaluation of a second screen application, which is designed to supersede the traditional user control interface for primary screen interaction. We describe how NSMobile, our second screen application, can be used as a pervasive multimedia platform by integrating user experiences on both the second screen and primary screen. The quantitative and qualitative evaluation of user interactions with interactive TV content also contributes to the future design of second screen applications.

HTTP adaptive streaming (HAS) technology is becoming a popular vehicle for online video delivery. HAS applications often compete for network resources without any coordination between each other in a shared network. This leads to quality of experience (QoE) fluctuations and unfairness between end users. This paper introduces a user-level fairness model (UF) which exploits video quality, switching impact and cost efficiency as the fairness metrics to achieve user-level fairness in resource allocation. Experimental results demonstrate how this model is a foundation to orchestrate the resource consumption of HAS streams.

Network operators recognise that Content Delivery Networks are essential for meeting user Internet application and content demands. The infrastructure must be tightly integrated to provide request routing, content caching, load balancing, scalability and reliability, whilst minimising deployment time and complexity. A major step towards achieving these goals is to embrace recent Software Defined Network and Network Function Virtualisation objectives and design principles. This paper outlines the OpenCache API; an interface used to define the behaviour and operation of an SDN-based content delivery platform in real-time. We demonstrate the applicability and effectiveness of such an API by implementing load-balancing and fail-over functionalities as part of an experimental deployment.

Living Labs provide a 'human-centric' research approach for the design of new ICT artefacts.In Living Labs users participate over several design stages, providing insights into unexpected ICT use, co-creation and evaluation of new IT solutions.Although this approach is becoming more popular, there is little comparative and reflective work on its practical dynamics, problems and possibilities.In this study, we analyse two 4-year Living Lab projects in Lancaster, UK and Siegen, Germany within the domain of Social TV, and compare experiences.We focus on documenting the purposes, methods and user dynamics that affect the trajectory of such long-term research initiatives, focusing inter alia on the dynamics of researcher/user interaction and the developing issues of trust and managing expectations; emphasizing some often neglected ethical issues and the impact of users' individual characteristics and their role in the community dynamics of Living Labs. RESEARCH HIGHLIGHTS• Comparison and reflection of two 4-year Living Lab studies in domestic environments.• Investigation of dynamics of researcher-user interaction.• Emphasizing ethical issues of long-term Living Lab research projects.

High quality online video streaming, both live and on-demand, has become an essential part of consumers' every-day lives. The popularity of video streaming has placed a heavy burden on the network infrastructure that now has to transfer an enormous amount of data very quickly to the end-user. To further exacerbate the situation, the Video-on-Demand (VoD) distribution paradigm uses a unicast independent flow for each user request. This results in multiple duplicate flows carrying the same video assets many times end-to-end. We present OpenCache: a highly configurable, efficient and transparent in-network caching service that aims to improve the VoD distribution efficiency by caching video assets as close to the end-user as possible. OpenCache leverages Software Defined Networking to benefit last mile environments by improving network utilisation and increasing the Quality of Experience for the end-user. Our evaluation on a pan-European OpenFlow testbed uses adaptive video streaming and demonstrates that with the use of OpenCache, the external link utilisation is reduced by 100%. Furthermore the streaming application receives better quality video and observes higher throughput, lower latency and shorter start up and buffering times.

Understanding the dynamics of user interactions and the behaviour of users as they browse for content is vital for advancements in content discovery, service personalisation, and recommendation engines which ultimately improve quality of user experience. In this paper, we analyse how more than 1,100 users browse an online TV service over a period of six months. Through the use of model-based clustering, we identify distinctive groups of users with discernible browsing patterns that vary during the course of the day.

This paper presents a dataset of user statistics collected from a P2P multimedia service infrastructure that delivers both live and on-demand content in high quality to users via different platforms: PC/Mac, and set top boxes. The dataset covers a period of seven months starting from October 2011, exposing a total of over 94k system statistic reports from thousands of user devices at a fine granularity. Such rich data source is made available to fellow researchers to aid in developing better understanding of video delivery mechanisms, user behaviour, and programme popularity evolution.

The European market for roaming services has historically experienced poor or no competition and, when competition has occurred, prices have been largely resistant to its downward pressure [8]. The root cause of this failure lies in the technical limitations of the existing roaming architecture. An architecture able to remove these limitations should allow the contractual mobility of roaming users: users should be able to select their access provider automatically, on a per-session basis and on one-to-one contractual basis. In this paper, we study the extent to which, by removing these constraints, the enablement of contractual mobility influences user and provider behaviour, creating an improved market outcome. To this end, we develop a model of the relationships between contractual mobility as a design choice for roaming, the behaviour of market players in response to that choice, and market outcomes. We then study its evolution over time as design choices and settings change in different experiment scenarios. Our results show that enabling contractual mobility - either as a commercial choice by providers or mandated by the regulator - creates similar incentives for both potential users and new providers to enter the market, while decreasing switching barriers. These encouraging, albeit seminal results could form the basis for the development of a techno-economic model of roaming aimed to reduce the uncertainty as to the timeliness and efficacy of national regulators' policies.

Video-on-demand (VoD) is becoming a popular service for commercial content distribution by offering end users the freedom to access recorded programmes. The management of on-demand assets is essential to maximise the efficiency of storage and network utilisation as well as advertisement. This paper introduces our recent efforts in design and implementation of an adaptive VoD archive system in an IPTV infrastructure. The system exploits live statistics on the user behaviours as well as the dynamic popularity of VoD programmes. Using the modelled programme popularity function, the VoD archive is capable of managing the VoD repository by adapting to the most recent user requests. The design has greatly improved the activity of VoD repository and user experience in on-demand services.

The paper introduces NextShareMobile, an integrated second screen application. Design and evaluations are presented.

This paper introduces the recent design and development of a converged IPTV service that has been deployed within a live test-bed (Living Lab) at Lancaster University for thousands of students. High quality audio-visual content is distributed over heterogeneous IP-based content networks, on both set-top box and web-based platforms. Peer-to-peer (P2P) technologies are exploited to provide energy efficient and low-cost delivery for commercial and user-generated content. The infrastructure and functional components are first presented exploring a number of key designs that facilitate the entire eco-system of content ingest, transcoding, P2P tracking, distribution, statistics, end systems, as well as integration of social networking. Due to the dynamic nature of P2P distribution, a quality measurement service with respect to user experience is also essential for the service evaluation and diagnosis. A multimodal QoE measurement framework which evaluates the IPTV services by collaborating measurements with a variety of different aspects is presented. Results of a use case are also described to verify the effectiveness of the measurement framework in exploiting relevant metrics from service components.

Peer-to-Peer (P2P) technologies provide efficient and low-cost delivery for commercial and user-generated content. Although a number of audio-visual content distribution services have been developed using P2P-based mechanisms, it is still a challenge to guarantee satisfactory user experience on high quality video streaming services due to the dynamic nature of P2P distribution. An objective assessment service is the key enabler of service quality monitoring and management. However, this complex task can not be achieved by any individual objective model that only captures a certain aspect of service quality. This paper introduces a multimodal quality evaluation framework that is specifically designed and implemented for the assessment of video streaming services in P2P-based IPTV systems. Results of initial experiments show the effectiveness of this framework.

In this paper we describe our experiences of developing and monitoring an Internet Television (IPTV) service in order to determine to what extent the system can provide useful hints (or triggers) that are exploitable by Content Distribution Networks (CDNs). Uniquely, our system integrates with social networking services and builds on the social graph to help form an understanding of user behaviour and potentially socially-motivated trends. By combining these attributes we are able to represent a social user journey for each user of the system which we seek to exploit for identifying potential trigger conditions. The paper concludes with an initial analysis of data collected during a five month trial of the service with staff and students of our University.

Wireless mesh networks are being used to provide Internet access in a cost efficient manner. Typically, consumer-level wireless access points with modified software are used to route traffic to potentially multiple back-haul points. Malware infected computers generate malicious traffic, which uses valuable network resources and puts other systems at risk. Intrusion detection systems can be used to detect such activity. Cost constraints and the decentralised nature of WMNs make performing intrusion detection on mesh devices desirable. However, these devices are typically resource constrained. This paper describes the results of examining their ability to perform intrusion detection. Our experimental study shows that commonly-used deep packet inspection approaches are unreliable on such hardware. We implement a set of lightweight anomaly detection mechanisms as part of an intrusion detection system, called OpenLIDS. We show that even with the limited hardware resources of a mesh device, it can detect current malware behaviour in an efficient way.

Wireless mesh networks are being increasingly used to provide affordable network connectivity to communities where wired deployment strategies are either not possible or are prohibitively expensive. Unfortunately, computer networks (including mesh networks) are frequently being exploited by increasingly profit-driven and insidious attackers, which can affect their utility for legitimate use. In response to this, a number of countermeasures have been developed, including intrusion detection systems that aim to detect anomalous behaviour caused by attacks. We present a set of socio-technical challenges associated with developing an intrusion detection system for a community wireless mesh network. The attack space on a mesh network is particularly large; we motivate the need for and describe the challenges of adopting an asset-driven approach to managing this space. Finally, we present an initial design of a modular architecture for intrusion detection, highlighting how it addresses the identified challenges.

This paper documents some of the socio-technical issues involved in developing security measures for a community environment, looking at the users of a wireless mesh network deployed within a rural village in north west England. We adopt an interdisciplinary methodological approach in eliciting requirements, to analyse the success of treating a community as an 'organisation' and implementing an approach, an OCTAVE method; in its original form designed to uncover security elements for businesses. Using a focus group technique we chart some of the assets and security concerns of the community members, leading to a greater understanding of people's perceptions of security, both personally and within the community, and the role of security in their regular computer usage.

Most Distributed Hash Tables (DHTs) simply consider interconnecting homogeneous nodes on the same overlay. However, realistically nodes on a network are heterogeneous in terms of their capabilities. Because of this, traditional DHTs have been shown to exhibit poor performance in a real-world environment. Additionally, we believe that it is this approach that contributes to a limited exploitation of peer-to-peer technologies. Previous work on super-peers in DHTs was proposed to address these performance issues, however the strategy used is often based on locally clustering peers around individual super-peers. This method of super-peering, however, compromises fundamental features such as load-balancing, resilience and routing efficiency, which traditional DHTs originally promised to offer.

peer reviewed

This paper describes a prototype application which enables the real-time monitoring and visualization of large Wide Area Networks (WANs) using smartphone devices. The techniques employed allow field engineers to rapidly gain access to a large information repository through the use of a camera equipped mobile phone. More specifically, the use of visual codes [11] attached to networking hardware and infrastructure cabling enables the real-time visualization of network traffic and statistics to be triggered by the capturing of images from a personal device. Moreover, the location and orientation of the phone are used as contextual parameters in order to control the specific information to be retrieved. The prototype described in this paper is currently under evaluation by Information Systems Services (ISS) which is responsible for network support across Lancaster University, the student residences network and also a large regional WAN spanning the whole of the North West of England. Our aim was to establish whether or not this user interaction technique could be harnessed for a real world application that would benefit field engineers who are responsible for maintaining a live production network interconnecting tens of thousands of hosts.