Run-time risk management in adaptive ICT systems

We will present results of the SERSCIS project related to risk management and mitigation strategies in adaptive multi-stakeholder ICT systems. The SERSCIS approach involves using semantic threat models to support automated design-time threat identification and mitigation analysis. The focus of this paper is the use of these models at run-time for automated threat detection and diagnosis. This is based on a combination of semantic reasoning and Bayesian inference applied to run-time system monitoring data. The resulting dynamic risk management approach is compared to a conventional ISO 27000 type approach, and validation test results presented from an Airport Collaborative Decision Making (A-CDM) scenario involving data exchange between multiple airport service providers

Identifying privacy risks in distributed data services:A model-driven approach

Online services are becoming increasingly data-centric; they collect, process, analyze and anonymously disclose growing amounts of personal data. It is crucial that such systems are engineered in a privacy-aware manner in order to satisfy both the privacy requirements of the user, and the legal privacy regulations that the system operates under. How can system developers be better supported to create privacy-aware systems and help them to understand and identify privacy risks? Model-Driven Engineering (MDE) offers a principled approach to engineer systems software. The capture of shared domain knowledge in models and corresponding tool support can increase the developers' understanding. In this paper, we argue for the application of MDE approaches to engineer privacy-aware systems. We present a general purpose privacy model and methodology that can be used to analyse and identify privacy risks in systems that comprise both access control and data pseudonymization enforcement technologies. We evaluate this method using a case-study based approach and show how the model can be applied to engineer privacy-aware systems and privacy policies that reduce the risk of unintended disclosure

Crime open data aggregation and management for the design of safer spaces in urban environments

This paper describes the major research and development activities which have been achieved so far since the launch of the DESURBS project (www.desurbs.eu) in 2011. The project focuses on the development of a Decision-Support System Portal (DSSP) which integrates information, data and software modules representing city assets, hazards and processing models that simulate exposures to risks and potential compromise to safety and security. The use of the DSSP will aid the design of safer and more resilient urban spaces. Specifically, it provides security related scenarios with contextual information to support various types of users who specialise in urban spatial design and planning. The DSSP is a web enabled system which is also adapted to mobile devices usage. It is supported with geographic maps and visualised aggregated data from a number of heterogeneous sources. A responsive web design which adapts to the resolution of smart mobile devices has also been achieved. That is, low powered mobiles can still provide map oriented data in a responsive fashion, while using multiple platforms (Android and iOS currently). The first DSSP prototype employs the United Kingdom crime statistics feed of year 2012 and analyses crime trends in 13 English Cities (including Greater London) which are distributed into four major-regions. The DSSP displays raw crime data via a marker on a map, while they are aggregated under specific crime type threads and visualised as “heat maps”. The specific visualisations are aligned to the various administrative regions such as neighbourhoods, catchments and postcodes. It also allows users to explore historical crime trends for a region over time, where crime statistics are contrasted. The scalability of the DSSP was also tested under increasingly large datasets and numbers of users, with tested loads on the map server and the main Django user application. The difference in speed between the mobile and desktop interfaces for a defined set of tasks using the application shall also be performed and presented in the near futur

Data Rate Performance Measurements of NFV Cloud Native Scaling for a Media Application

This dataset accompanies a paper that discusses the advantages of a 3GPP-compliant service-based architecture platform that demonstrates the concept of cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost. The discussion is motivated by a customer-facing trial conducted in Bristol, UK where the platform was deployed as virtual network functions and the vertical service using the cloud-native orchestrator. The architecture of the system is presented, together with an exemplary media application illustrating the benefits of dynamic control of the whole service function. Three scenarios are described: a baseline case, where a single service instance handles requests from multiple clients at different locations, which can become overloaded with a consequent degradation in user experience; a static horizontally scaled-out service scenario, where service instances serving content are placed closest to users on edge hosts with associated performance gains in terms of reduced response time and networking costs, but with increased hosting costs; and a dynamically-managed horizontal scaling case, where storage service instances are enabled automatically based on location-specific demands when needed. It is illustrated that dynamic service instances based on sensing QoS metrics provides an opportunity to achieve a balance between user experience benefits of edge service provisioning and acceptable costs through avoiding waste of cloud resources at the edge.</span

Horizontal Scaling Media Transfer Performance Experiment Data

This dataset provides performance data rate&nbsp;measurements a 3GPP-compliant service-based architecture platform that demonstrates the concept of&nbsp;cloud-native service orchestration and routing for a media vertical sector application. Cloud-native service orchestration and routing&nbsp;is a complete end-to-end approach that enables virtualisation and management of multiple layers in the OSI model, which provides&nbsp;considerable flexibility and control to achieve delivery of QoS to users in the face of varying demand, at reasonable cost.&nbsp; </span

Advanced data analytics and visualisation for the management of human perception of safety and security in urban spaces

The genesis of this work began during the DESURBS project. The scope of the project was to help build a collaborative decision-support system portal where spatial planning professionals could learn about designing much more secure and safer spaces in urban areas. The portal achieved this via integrating a number of tools under a common, simple to use, interface. However, the deficiencies in the project became apparent with subsequent development. Many of the open data employed changed format while applications were increasingly custom built for a single dataset. In order to overcome this a system called KnowDS was redesigned. The essence of the new design includes decoupling acquisition, analysis and overall presentation of data components. The acquisition component was designed to snap-shot the “data providing methods” and query data provenance in a similar way to a source code repository. The analysis component is built under a number of modular tools with a common interface which allows analysis to build in a plug&amp;play approach. Finally, the data presentation component is where the custom logic goes. Under such design approach, the building of future applications becomes less challenging. As a consequence, two case studies using the new framework were considered. Firstly, a UK crime web-browser which allows data analytics performances at various granularities of crime types while correlating crimes across various UK cities has been achieved. Secondly, a mobile application which enables to generate reports on citizens’ perception of safety in urban zones has also been developed. The two applications were efficiently built under the new design framework; and they clearly demonstrate the capacity of the new system while they actively generate new knowledge about safety in urban space