Revised reference model

This document contains an update of the HIDENETS Reference Model, whose preliminary version was introduced in D1.1. The Reference Model contains the overall approach to development and assessment of end-to-end resilience solutions. As such, it presents a framework, which due to its abstraction level is not only restricted to the HIDENETS car-to-car and car-to-infrastructure applications and use-cases. Starting from a condensed summary of the used dependability terminology, the network architecture containing the ad hoc and infrastructure domain and the definition of the main networking elements together with the software architecture of the mobile nodes is presented. The concept of architectural hybridization and its inclusion in HIDENETS-like dependability solutions is described subsequently. A set of communication and middleware level services following the architecture hybridization concept and motivated by the dependability and resilience challenges raised by HIDENETS-like scenarios is then described. Besides architecture solutions, the reference model addresses the assessment of dependability solutions in HIDENETS-like scenarios using quantitative evaluations, realized by a combination of top-down and bottom-up modelling, as well as verification via test scenarios. In order to allow for fault prevention in the software development phase of HIDENETS-like applications, generic UML-based modelling approaches with focus on dependability related aspects are described. The HIDENETS reference model provides the framework in which the detailed solution in the HIDENETS project are being developed, while at the same time facilitating the same task for non-vehicular scenarios and application

Context-aware adaptation in DySCAS

DySCAS is a dynamically self-configuring middleware for automotive control systems. The addition of autonomic, context-aware dynamic configuration to automotive control systems brings a potential for a wide range of benefits in terms of robustness, flexibility, upgrading etc. However, the automotive systems represent a particularly challenging domain for the deployment of autonomics concepts, having a combination of real-time performance constraints, severe resource limitations, safety-critical aspects and cost pressures. For these reasons current systems are statically configured. This paper describes the dynamic run-time configuration aspects of DySCAS and focuses on the extent to which context-aware adaptation has been achieved in DySCAS, and the ways in which the various design and implementation challenges are met

Resilience Strategies for Network Challenge Detection, Identification and Remediation

The enormous growth of the Internet and its use in everyday life make it an attractive target for malicious users. As the network becomes more complex and sophisticated it becomes more vulnerable to attack. There is a pressing need for the future internet to be resilient, manageable and secure. Our research is on distributed challenge detection and is part of the EU Resumenet Project (Resilience and Survivability for Future Networking: Framework, Mechanisms and Experimental Evaluation). It aims to make networks more resilient to a wide range of challenges including malicious attacks, misconfiguration, faults, and operational overloads. Resilience means the ability of the network to provide an acceptable level of service in the face of significant challenges; it is a superset of commonly used definitions for survivability, dependability, and fault tolerance. Our proposed resilience strategy could detect a challenge situation by identifying an occurrence and impact in real time, then initiating appropriate remedial action. Action is autonomously taken to continue operations as much as possible and to mitigate the damage, and allowing an acceptable level of service to be maintained. The contribution of our work is the ability to mitigate a challenge as early as possible and rapidly detect its root cause. Also our proposed multi-stage policy based challenge detection system identifies both the existing and unforeseen challenges. This has been studied and demonstrated with an unknown worm attack. Our multi stage approach reduces the computation complexity compared to the traditional single stage, where one particular managed object is responsible for all the functions. The approach we propose in this thesis has the flexibility, scalability, adaptability, reproducibility and extensibility needed to assist in the identification and remediation of many future network challenges