Threats Management Throughout the Software Service Life-Cycle

Software services are inevitably exposed to a fluctuating threat picture. Unfortunately, not all threats can be handled only with preventive measures during design and development, but also require adaptive mitigations at runtime. In this paper we describe an approach where we model composite services and threats together, which allows us to create preventive measures at design-time. At runtime, our specification also allows the service runtime environment (SRE) to receive alerts about active threats that we have not handled, and react to these automatically through adaptation of the composite service. A goal-oriented security requirements modelling tool is used to model business-level threats and analyse how they may impact goals. A process flow modelling tool, utilising Business Process Model and Notation (BPMN) and standard error boundary events, allows us to define how threats should be responded to during service execution on a technical level. Throughout the software life-cycle, we maintain threats in a centralised threat repository. Re-use of these threats extends further into monitoring alerts being distributed through a cloud-based messaging service. To demonstrate our approach in practice, we have developed a proof-of-concept service for the Air Traffic Management (ATM) domain. In addition to the design-time activities, we show how this composite service duly adapts itself when a service component is exposed to a threat at runtime.Comment: In Proceedings GraMSec 2014, arXiv:1404.163

Modelling, validating, and ranking of secure service compositions

This is the author accepted manuscript. The final version is available from the publisher via the DOI in this recordIn the world of large-scale applications, software as a service (SaaS) in general and use of microservices, in particular, is bringing service-oriented architectures to a new level: Systems in general and systems that interact with human users (eg, sociotechnical systems) in particular are built by composing microservices that are developed independently and operated by different parties. At the same time, SaaS applications are used more and more widely by enterprises as well as public services for providing critical services, including those processing security or privacy of relevant data. Therefore, providing secure and reliable service compositions is increasingly needed to ensure the success of SaaS solutions. Building such service compositions securely is still an unsolved problem. In this paper, we present a framework for modelling, validating, and ranking secure service compositions that integrate both automated services as well as services that interact with humans. As a unique feature, our approach for ranking services integrates validated properties (eg, based on the result of formally analysing the source code of a service implementation) as well as contractual properties that are part of the service level agreement and, thus, not necessarily ensured on a technical level

Comparison of STS and ArchiMate Risk and Security Overlay

ArchiMate'i kasutatakse tänapäeval laialdaselt erinevates ärivaldkondades ettevõttesüsteemide arhitektuuri modelleerimiseks ning seda võib iseloomustada modelleerimise tööriistana, mis ühendab endas UML'i ja BPMN'i. STS keskendub aga sotsiotehnilisele perspektiivile ja tegijatevahelistele sotsiaalsetele vastastikmõjudele. Kuigi neil on palju ühist, on tegemist siiski erinevate lähenemistega, mistõttu räägitakse tänapäeval ArchiMate'st ja Secure Socio-Technical Systems'ist valdavalt kui eraldiseisvatest süsteemidest. Sellise olukorra tõttu on tekkinud puudujääk tööriistadest ja lähenemistest, mis ühendaks kaks süsteemi üheks uueks, mis võtaks arvesse nii modelleerimise arhitektuurseid kui ka sotsiotehnilisi aspekte. Selline kombinatsioon võib osutuda kasulikuks, kuna ArchiMate'ga saab modelleerida riskijuhtimist ja STS abil saab modelleerida erinevate süsteemi kaasatud tegijate omavahelist suhtlemist sotsiaalsest vaatevinklist ja turvalisuse inimfaktorit. Seega nende kahe süsteemi ühendamise teel võib luua turvalisuse modelleerimise lähenemise, mis katab nii arhitektuurilised kui sotsiaalsed vaatevinklid. Ideaalselt kasutaks selline lähenemine mõlema süsteemi tugevamaid külgi ja lahendaks mõned kitsaskohad. Lähenemise terviklikust hinnatakse ISSRM'i suhtes. Selles lõputöös kirjeldatakse ülalmainitud kombineeritud lähenemist turvalisuse modelleerimisele.Nowadays ArchiMate is widely used in enterprise architecture modelling of the various business domains and briefly could be described as something in between UML and BPMN with main focus in architectural perspective. STS in its turn is focusing on socio-technical perspective and taking into consideration social interactions betwen actors. Current state of the art is talking about Secure Socio-Technical Systems and ArchiMate separately. This is perfectly fine because this two approaches are quite different. Still, they have a lot in common. Based on the state described above problem could be identified as an absence of tools or approaches which will combine these two approaches into a new one, which will take into consideration both architectural and socio-technical perspectives of modelling. This combination could be beneficial because ArchiMate risk and security overlay models risk management and STS models how actors involved in this system interact with each other from social point of view and highlights “human factor” in security. Thus, combination of them could potentially result in security modelling approach which will cover both architecture and social points of view. Ideally, this approach will create some workarounds over weak places in both initial approaches and heavily use their best parts. We will also validate this approach in terms of completeness with respect to ISSRM. In this paper we will describe this combined approach

Enabling quantitative data analysis through e-infrastructures

This paper discusses how quantitative data analysis in the social sciences can engage with and exploit an e-Infrastructure. We highlight how a number of activities which are central to quantitative data analysis, referred to as ‘data management’, can benefit from e-infrastructure support. We conclude by discussing how these issues are relevant to the DAMES (Data Management through e-Social Science) research Node, an ongoing project that aims to develop e-Infrastructural resources for quantitative data analysis in the social sciences

Legal linked data ecosystems and the rule of law

This chapter introduces the notions of meta-rule of law and socio-legal ecosystems to both foster and regulate linked democracy. It explores the way of stimulating innovative regulations and building a regulatory quadrant for the rule of law. The chapter summarises briefly (i) the notions of responsive, better and smart regulation; (ii) requirements for legal interchange languages (legal interoperability); (iii) and cognitive ecology approaches. It shows how the protections of the substantive rule of law can be embedded into the semantic languages of the web of data and reflects on the conditions that make possible their enactment and implementation as a socio-legal ecosystem. The chapter suggests in the end a reusable multi-levelled meta-model and four notions of legal validity: positive, composite, formal, and ecological

An exploration of IoT platform development

IoT (Internet of Things) platforms are key enablers for smart city initiatives, targeting the improvement of citizens\u27 quality of life and economic growth. As IoT platforms are dynamic, proactive, and heterogeneous socio-technical artefacts, systematic approaches are required for their development. Limited surveys have exclusively explored how IoT platforms are developed and maintained from the perspective of information system development process lifecycle. In this paper, we present a detailed analysis of 63 approaches. This is accomplished by proposing an evaluation framework as a cornerstone to highlight the characteristics, strengths, and weaknesses of these approaches. The survey results not only provide insights of empirical findings, recommendations, and mechanisms for the development of quality aware IoT platforms, but also identify important issues and gaps that need to be addressed