We discuss the problem of accountability when multiple parties cooperate towards an end result, such as multiple companies in a supply chain or departments of a government service under different authorities. In cases where a fully trusted central point does not exist, it is difficult to obtain a trusted audit trail of a workflow when each individual participant is unaccountable to all others. We propose AudiWFlow, an auditing architecture that makes participants accountable for their contributions in a distributed workflow. Our scheme provides confidentiality in most cases, collusion detection, and availability of evidence after the workflow terminates. AudiWFlow is based on verifiable secret sharing and real-time peer-to-peer verification of records; it further supports multiple levels of assurance to meet a desired trade-off between the availability of evidence and the overhead resulting from the auditing approach. We propose and evaluate two implementation approaches for AudiWFlow. The first one is fully distributed except for a central auxiliary point that, nevertheless, needs only a low level of trust. The second one is based on smart contracts running on a public blockchain, which is able to remove the need for any central point but requires integration with a blockchain.

Current research in the area of concept learning makes use of deep learning and ensembles methods to learn concepts. Concept learning allows us to combine heterogeneous entities in data which could collectively identify as individual concepts. Heterogeneity and compositionality are crucial areas to explore in machine learning as it has the potential to contribute profoundly to artificial general intelligence. We investigate the use of spiking neural networks for concept learning. Spiking neurones inclusively model the temporal properties as observed in biological neurones. A benefit of spike-based neurones allows for localised learning rules that only adapts connections between relevant neurones. In this position paper, we propose a technique allowing dynamic formation of synapse (connections) in spiking neural networks, the basis of structural plasticity. Achieving dynamic formation of synapse allows for a unique approach to concept learning with a malleable neural structure. We call this technique Neurosymbolic Spike-Concept Learner (NS-SCL). The limitations of NS-SCL can be overcome with the neuromorphic computing paradigm. Furthermore, introducing NS-SCL as a technique on neuromorphic platforms should motivate a new direction of research towards Neuromorphic General Intelligence (NGI), a term we define to some extent.

Ensuring the preservation of security is a key requirement and challenge for Service-Based Systems (SBS) due to the use of third party software services not operating under different security perimeters. In this paper, we present an approach for verifying the security properties of SBS workflows and adapting them if such properties are not preserved. Our approach uses secure service composition patterns. These patterns encode proven dependencies between service level and workflow level security properties. These dependencies are used in reasoning processes supporting the verification of SBS workflows with respect to workflow security properties and their adaptation in ways that guarantee the properties if necessary. Our approach has been implemented by extending the Eclipse BPEL Designer and validated experimentally. The experimental evaluation has produced positive results, indicating that even for complex workflows and large sets of secure service composition patterns, verification can be performed efficiently.

Cyber threat intelligence sharing has become a focal point for many organizations to improve resilience against cyberattacks. The objective lies in sharing relevant information achieved through automating as many processes as possible without losing control or compromising security. The intelligence may be crowdsourced from decentralized stakeholders to collect and enrich existing information. Trust is an attribute of actionable cyber threat intelligence that has to be established between stakeholders. Sharing information about vulnerabilities requires a high level of trust because of the sensitive information. Some threat intelligence platforms/providers support trust establishment through internal vetting processes; others rely on stakeholders to manually build up trust. The latter may reduce the amount of intelligence sources. This work presents a novel trust taxonomy to establish a trusted threat sharing environment. 30 popular threat intelligence platforms/providers were analyzed and compared regarding trust functionalities. Trust taxonomies were analyzed and compared. Illustrative case studies were developed and analyzed applying our trust taxonomy.

In this paper we propose a framework for proactive SLA negotiation that integrates this process with dynamic service discovery and, hence, can provide integrated runtime support for both these key activities which are necessary in order to achieve the runtime operation of service based systems with minimised interruptions. More specifically, our framework discovers candidate constituent services for a composite service, establishes an agreed but not enforced SLA and a period during which this pre-agreement can be activated should this become necessary.

In this paper we describe a platform that supports context aware runtime service discovery. The platform supports service discovery based on structural and behavioural service models as well as complex context related service discovery conditions which are specified in a newly introduced query language. During discovery, context information is obtained through a uniform scheme of calling "context operations" and is subsequently used in the evaluation of service discovery queries.

This paper presents a framework for monitoring the compliance of systems composed of Web-services with requirements set for them at runtime. This framework assumes systems composed of Web-services which are co-coordinated by a service composition process expressed in BPEL and uses event calculus to specify the requirements to be monitored. These requirements may include behavioral properties of a system which are automatically extracted from the specification of its composition process in BPEL and/or assumptions that system providers can specify in terms of events extracted from this specification.

This paper describes a framework supporting the runtime monitoring of requirements for systems implemented as compositions of Web-services specified in BPEL. The requirements that can be monitored are specified in event calculus. The paper presents an overview of the framework and describes the architecture and implementation of a tool that we have developed to operationalise it. It also presents the results of a preliminary experimental evaluation of the framework.

This paper proposes a framework for monitoring the compliance of systems composed of web-services with requirements set for th. This framework assumes systems composed of web-services that are co-ordinated by a service composition process expressed in BPEL4WS and uses event calculus to specify the properties to be monitored. The monitorable properties may include behavioural properties of a syst which are automatically extracted from the specification of its composition process in BPEL4WS and/or assumptions that syst providers can specify in terms of events extracted from this specification.