On the Robust Synthesis of Logical Consensus Algorithms for Distributed Intrusion Detection

We introduce a novel consensus mechanism by which the agents of a network can reach an agreement on the value of a shared logical vector function depending on binary input events. Based on results on the convergence of finite-state iteration systems, we provide a technique to design logical consensus systems that minimizing the number of messages to be exchanged and the number of steps before consensus is reached, and tolerating a bounded number of failed or malicious agents. We provide sufficient joint conditions on the input visibility and the communication topology for the method’s applicability. We describe the application of our method to two distributed network intrusion detection problems

A Self-Routing Protocol for Distributed Consensus on Logical Information

In this paper, we address decision making problems, depending on a set of input events, with networks of dynamic agents that have partial visibility of such events. Previous work by the authors proposed so-called logical consensus approach, by which a network of agents, that can exchange binary values representing their local estimates of the events, is able to reach a unique and consistent decision. The approach therein proposed is based on the construction of an iterative map, whose computation is centralized and guaranteed under suitable conditions on the input visibility and graph connectivity. Under the same conditions, we extend the approach in this work by allowing the construction of a logical linear consensus system that is globally stable in a fully distributed way. The effectiveness of the proposed method is showed through the real implementation of a wireless sensor network as a framework for the surveillance of an urban area

Distributed motion misbehavior detection in teams of heterogeneous aerial robots

This paper addresses the problem of detecting possible misbehavior in a group of autonomous mobile robots, which coexist in a shared environment and interact with each other and coordinate according to a set of common interaction rules. Such rules specify what actions each robot is allowed to perform in order to interact with the other members of the group. The rules are distributed, i.e., they can be evaluated only starting from the knowledge of the individual robot and the information the robot gathers from neighboring robots. We consider misbehaving those robots which, because of either spontaneous failures or malicious tampering, do not follow the rules and whose behavior thus deviates from the nominal assigned one. The main contribution of the paper is to provide a methodology to detect such misbehavior by observing the congruence of actual behavior with the assigned rules as applied to the actual state of the system. The presented methodology is based on a consensus protocol on the events observed by robots. The methodology is fully distributed in the sense that it can be performed by individual robots based only on the local available information, it has been theoretically proven and validated with experiments involving real aerial heterogeneous robots

Leader-following Consensus of Multi-agent Systems over Finite Fields

The leader-following consensus problem of multi-agent systems over finite fields ${\mathbb F}_p$ is considered in this paper. Dynamics of each agent is governed by a linear equation over ${\mathbb F}_p$, where a distributed control protocol is utilized by the followers.Sufficient and/or necessary conditions on system matrices and graph weights in ${\mathbb F}_p$ are provided for the followers to track the leader

"Going back to our roots": second generation biocomputing

Researchers in the field of biocomputing have, for many years, successfully "harvested and exploited" the natural world for inspiration in developing systems that are robust, adaptable and capable of generating novel and even "creative" solutions to human-defined problems. However, in this position paper we argue that the time has now come for a reassessment of how we exploit biology to generate new computational systems. Previous solutions (the "first generation" of biocomputing techniques), whilst reasonably effective, are crude analogues of actual biological systems. We believe that a new, inherently inter-disciplinary approach is needed for the development of the emerging "second generation" of bio-inspired methods. This new modus operandi will require much closer interaction between the engineering and life sciences communities, as well as a bidirectional flow of concepts, applications and expertise. We support our argument by examining, in this new light, three existing areas of biocomputing (genetic programming, artificial immune systems and evolvable hardware), as well as an emerging area (natural genetic engineering) which may provide useful pointers as to the way forward.Comment: Submitted to the International Journal of Unconventional Computin

Dagstuhl News January - December 2008

"Dagstuhl News" is a publication edited especially for the members of the Foundation "Informatikzentrum Schloss Dagstuhl" to thank them for their support. The News give a summary of the scientific work being done in Dagstuhl. Each Dagstuhl Seminar is presented by a small abstract describing the contents and scientific highlights of the seminar as well as the perspectives or challenges of the research topic

Event-Driven Network Programming

Software-defined networking (SDN) programs must simultaneously describe static forwarding behavior and dynamic updates in response to events. Event-driven updates are critical to get right, but difficult to implement correctly due to the high degree of concurrency in networks. Existing SDN platforms offer weak guarantees that can break application invariants, leading to problems such as dropped packets, degraded performance, security violations, etc. This paper introduces EVENT-DRIVEN CONSISTENT UPDATES that are guaranteed to preserve well-defined behaviors when transitioning between configurations in response to events. We propose NETWORK EVENT STRUCTURES (NESs) to model constraints on updates, such as which events can be enabled simultaneously and causal dependencies between events. We define an extension of the NetKAT language with mutable state, give semantics to stateful programs using NESs, and discuss provably-correct strategies for implementing NESs in SDNs. Finally, we evaluate our approach empirically, demonstrating that it gives well-defined consistency guarantees while avoiding expensive synchronization and packet buffering

Behavior Classification, Security, and Consensus in Societies of Robots

This thesis addresses some fundamental issues toward the realization of "societies" of robots. This objective requires dealing with large numbers of heterogenous autonomous systems, differing in their bodies, sensing and intelligence, that are made to coexist, communicate, learn and classify, and compete fairly, while achieving their individual goals. First, as in human or animal societies, robots must be able to perform cooperative "behaviors" that involve coordination of their actions, based on their own goals, proprioceptive sensing, and information they can receive from other neighboring robots. An effective way to successfully achieve cooperation is obtained by requiring that robots share a set of decentralized motion "rules" involving only locally available data. A first contribution of the thesis consists in showing how these behaviors can be nicely described by a suitable hybrid formalism, including the heterogenous dynamics of every robots and the above mentioned rules that are based on events. A second contribution deals with the problem of classifying a set of robotic agents, based on their dynamics or the interaction protocols they obeys, as belonging to different "species". Various procedures are proposed allowing the construction of a distributed classification system, based on a decentralized identification mechanism, by which every agent classifies its neighbors using only locally available information. By using this mechanism, members of the society can reach a consensus on the environment and on the integrity of the other neighboring robots, so as to improve the overall security of the society. This objective involves the study of convergence of information that is not represented by real numbers, as often in the literature, rather by sets. The dynamics of the evolution of information across a number of robots is described by set-valued iterative maps. While the study of convergence of set-valued iterative maps is highly complex in general, this thesis focuses on Boolean maps, which are comprised of arbitrary combinations of unions, intersections, and complements of sets. Through the development of an industrial robotic society, it is finally shown how the proposed technique applies to a real and commercially relevant case-study. This society sets the basis for a full-fledged factory of the future, where the different and heterogeneous agents operate and interact using a blend of autonomous skills, social rules, and central coordination