Ensuring Cyber-Security in Smart Railway Surveillance with SHIELD

Modern railways feature increasingly complex embedded computing systems for surveillance, that are moving towards fully wireless smart-sensors. Those systems are aimed at monitoring system status from a physical-security viewpoint, in order to detect intrusions and other environmental anomalies. However, the same systems used for physical-security surveillance are vulnerable to cyber-security threats, since they feature distributed hardware and software architectures often interconnected by ‘open networks’, like wireless channels and the Internet. In this paper, we show how the integrated approach to Security, Privacy and Dependability (SPD) in embedded systems provided by the SHIELD framework (developed within the EU funded pSHIELD and nSHIELD research projects) can be applied to railway surveillance systems in order to measure and improve their SPD level. SHIELD implements a layered architecture (node, network, middleware and overlay) and orchestrates SPD mechanisms based on ontology models, appropriate metrics and composability. The results of prototypical application to a real-world demonstrator show the effectiveness of SHIELD and justify its practical applicability in industrial settings

Networked buffering: a basic mechanism for distributed robustness in complex adaptive systems

A generic mechanism - networked buffering - is proposed for the generation of robust traits in complex systems. It requires two basic conditions to be satisfied: 1) agents are versatile enough to perform more than one single functional role within a system and 2) agents are degenerate, i.e. there exists partial overlap in the functional capabilities of agents. Given these prerequisites, degenerate systems can readily produce a distributed systemic response to local perturbations. Reciprocally, excess resources related to a single function can indirectly support multiple unrelated functions within a degenerate system. In models of genome:proteome mappings for which localized decision-making and modularity of genetic functions are assumed, we verify that such distributed compensatory effects cause enhanced robustness of system traits. The conditions needed for networked buffering to occur are neither demanding nor rare, supporting the conjecture that degeneracy may fundamentally underpin distributed robustness within several biotic and abiotic systems. For instance, networked buffering offers new insights into systems engineering and planning activities that occur under high uncertainty. It may also help explain recent developments in understanding the origins of resilience within complex ecosystems. \ud \u

A Model of Emotion as Patterned Metacontrol

Adaptive systems use feedback as a key strategy to cope with uncertainty and change in their environments. The information fed back from the sensorimotor loop into the control architecture can be used to change different elements of the controller at four different levels: parameters of the control model, the control model itself, the functional organization of the agent and the functional components of the agent. The complexity of such a space of potential configurations is daunting. The only viable alternative for the agent ?in practical, economical, evolutionary terms? is the reduction of the dimensionality of the configuration space. This reduction is achieved both by functionalisation —or, to be more precise, by interface minimization— and by patterning, i.e. the selection among a predefined set of organisational configurations. This last analysis let us state the central problem of how autonomy emerges from the integration of the cognitive, emotional and autonomic systems in strict functional terms: autonomy is achieved by the closure of functional dependency. In this paper we will show a general model of how the emotional biological systems operate following this theoretical analysis and how this model is also of applicability to a wide spectrum of artificial systems

On the Topology Maintenance of Dynamic P2P Overlays through Self-Healing Local Interactions

This paper deals with the use of self-organizing protocols to improve the reliability of dynamic Peer-to-Peer (P2P) overlay networks. We present two approaches, that employ local knowledge of the 2nd neighborhood of nodes. The first scheme is a simple protocol requiring interactions among nodes and their direct neighbors. The second scheme extends this approach by resorting to the Edge Clustering Coefficient (ECC), a local measure that allows to identify those edges that connect different clusters in an overlay. A simulation assessment is presented, which evaluates these protocols over uniform networks, clustered networks and scale-free networks. Different failure modes are considered. Results demonstrate the viability of the proposal.Comment: A revised version of the paper appears in Proc. of the IFIP Networking 2014 Conference, IEEE, Trondheim, (Norway), June 201

Spare capacity modelling and its applications in survivable iP-over-optical networks

As the interest in IP-over-optical networks are becoming the preferred core network architecture, survivability has emerged as a major concern for network service providers; a result of the potentially huge traffic volumes that will be supported by optical infrastructure. Therefore, implementing recovery strategies is critical. In addition to the traditional recovery schemes based around protection and restoration mechanisms, pre-allocated restoration represents a potential candidate to effect and maintain network resilience under failure conditions. Preallocated restoration technique is particularly interesting because it provides a trade-off in terms of recovery performance and resources between protection and restoration schemes. In this paper, the pre-allocated restoration performance is investigated under single and dual-link failures considering a distributed GMPLSbased IP/WDM mesh network. Two load-based spare capacity optimisation methods are proposed in this paper; Local Spare Capacity Optimisation (LSCO) and Global Spare Capacity Optimisation (GSCO)