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Distributed Computation in Dynamic Networks
, 2009
"... In this paper we investigate distributed computation in dynamic networks in which the network topology changes from round to round. We consider a worstcase model in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current rou ..."
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In this paper we investigate distributed computation in dynamic networks in which the network topology changes from round to round. We consider a worstcase model in which the communication links for each round are chosen by an adversary, and nodes do not know who their neighbors for the current round are before they broadcast their messages. The model allows the study of the fundamental computation power of dynamic networks. In particular, it captures mobile networks and wireless networks, in which mobility and interference render communication unpredictable. In contrast to much of the existing work on dynamic networks, we do not assume that the network eventually stops changing; we require correctness and termination even in networks that change continually. We introduce a stability property called
Smart rewiring for network robustness
, 2013
"... While new forms of attacks are developed every day to compromise essential infrastructures, service providers are also expected to develop strategies to mitigate the risk of extreme failures. In this context, tools of network science have been used to evaluate network robustness and propose resilien ..."
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While new forms of attacks are developed every day to compromise essential infrastructures, service providers are also expected to develop strategies to mitigate the risk of extreme failures. In this context, tools of network science have been used to evaluate network robustness and propose resilient topologies against attacks. We present here a new rewiring method to modify the network topology, improving its robustness, based on the evolution of the network’s largest component during a sequence of targeted attacks. In comparison with previous strategies, our method lowers by several orders of magnitude the computational effort necessary to improve robustness. Our rewiring also drives the formation of layers of nodes with similar degree while keeping a highly modular structure. This ‘modular onionlike structure’ is a particular class of the onionlike structure previously described in the literature. We apply our rewiring strategy to an unweighted representation of the World Airtransportation network and show that an improvement of 30 % in its overall robustness can be achieved through smart swaps of around 9 % of its links.
Dense Subgraphs on Dynamic Networks
"... Abstract. In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense subtructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly ..."
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Abstract. In distributed networks, it is often useful for the nodes to be aware of dense subgraphs, e.g., such a dense subgraph could reveal dense subtructures in otherwise sparse graphs (e.g. the World Wide Web or social networks); these might reveal community clusters or dense regions for possibly maintaining good communication infrastructure. In this work, we address the problem of selfawareness of nodes in a dynamic network with regards to graph density, i.e., we give distributed algorithms for maintaining dense subgraphs that the member nodes are aware of. The only knowledge that the nodes need is that of the dynamic diameter D, i.e., the maximum number of rounds it takes for a message to traverse the dynamic network. For our work, we consider a model where the number of nodes are fixed, but a powerful adversary can add or remove a limited number of edges from the network at each time step. The communication is by broadcast only and follows the CONGEST model. Our algorithms are continuously executed on the network, and at any time (after some initialization) each node will be aware if it is part (or not) of a particular dense subgraph. We give algorithms that (2+ǫ)approximate the densest subgraph and (3+ǫ)approximate the atleastkdensest subgraph (for a given parameter k). Our algorithms work for a wide range of parameter values and run in O(Dlog 1+ǫ n) time. Further, a special case of our results also gives the first fully decentralized approximation algorithms for densest and atleastkdensest subgraph problems for static distributed graphs. 1
Distributed computation in wireless and dynamic networks
, 2012
"... Today’s wireless networks tend to be centralized: they are organized around a fixed central backbone such as a network of cellular towers or wireless access points. However, as mobile computing devices continue to shrink in size and in cost, we are reaching the point where largescale adhoc wireles ..."
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Today’s wireless networks tend to be centralized: they are organized around a fixed central backbone such as a network of cellular towers or wireless access points. However, as mobile computing devices continue to shrink in size and in cost, we are reaching the point where largescale adhoc wireless networks, composed of swarms of cheap devices or sensors, are becoming feasible. In this thesis we study the theoretical computation power of such networks, and ask what tasks are they capable of carrying out, how long does solving particular tasks take, and what is the effect of the unpredictable network topology on the network’s computation power. In the first part of the thesis we introduce an abstract model for dynamic networks. In contrast to much of the literature on mobile and adhoc networks, our model makes fairly minimalistic assumptions; it allows the network topology to change arbitrarily from round to round, as long as in each round the communication graph is connected. We show that even in this weak model, global computation is still possible, and any function of the nodes’ initial inputs can be computed efficiently. Also, using tools
SelfHealing of Byzantine Faults
"... Recent years have seen significant interest in designing networks that are selfhealing in the sense that they can automatically recover from adversarial attack. Previous work shows that it is possible for a network to automatically recover, even when an adversary repeatedly deletes nodes in the net ..."
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Recent years have seen significant interest in designing networks that are selfhealing in the sense that they can automatically recover from adversarial attack. Previous work shows that it is possible for a network to automatically recover, even when an adversary repeatedly deletes nodes in the network. However, there have not yet been any algorithms that selfheal in the case where an adversary takes over nodes. In this paper, we address this gap. In particular, we describe a communication network over n nodes that ensures the following properties, even when an adversary controls up to t ≤ (1/4 − ɛ)n nodes, for any positive ɛ. First, the network provides pointtopoint communication with bandwidth and latency costs that are asymptotically optimal. Second, O(t(log ∗ n) 2) message corruptions occur in expectation, before the adversarially controlled nodes are effectively quarantined so that they cause no more corruptions. We present empirical results showing that our approach may be practical. “Fool me once, shame on you. Fool me twice, shame on me. ” English proverb 1
SelfHealing Computation?
"... Abstract. In the problem of reliable multiparty computation (RC), there are n parties, each with an individual input, and the parties want to jointly compute a function f over n inputs. The problem is complicated by the fact that an omniscient adversary controls a hidden fraction of the parties. We ..."
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Abstract. In the problem of reliable multiparty computation (RC), there are n parties, each with an individual input, and the parties want to jointly compute a function f over n inputs. The problem is complicated by the fact that an omniscient adversary controls a hidden fraction of the parties. We describe a selfhealing algorithm for this problem. In particular, for a fixed function f, with n parties and m gates, we describe how to perform RC repeatedly as the inputs to f change. Our algorithm maintains the following properties, even when an adversary controls up to t ≤ ( 1 4 − )n parties, for any constant > 0. First, our algorithm performs each reliable computation with the following amortized resource costs: O(m+ n logn) messages, O(m+ n logn) computational operations, and O(`) latency, where ` is the depth of the circuit that computes f. Second, the expected total number of corruptions is O(t(log∗m)2), after which the adversarially controlled parties are effectively quarantined so that they cause no more corruptions.
Xheal: Localized Selfhealing using Expanders (Extended Abstract)
, 2011
"... We consider the problem of selfhealing in reconfigurable networks (e.g. peertopeer and wireless mesh networks) that are under repeated attack by an omniscient adversary and propose a fully distributed algorithm, Xheal, that maintains good expansion and spectral properties of the network, also kee ..."
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We consider the problem of selfhealing in reconfigurable networks (e.g. peertopeer and wireless mesh networks) that are under repeated attack by an omniscient adversary and propose a fully distributed algorithm, Xheal, that maintains good expansion and spectral properties of the network, also keeping the network connected. Moreover, Xheal, does this while allowing only low stretch and degree increase per node. The algorithm heals global properties like expansion and stretch while only doing local changes and using only local information. We use a model similar to that used in recent work on selfhealing. In our model, over a sequence of rounds, an adversary either inserts a node with arbitrary connections or deletes an arbitrary node from the network. The network responds by quick “repairs, ” which consist of adding or deleting edges in an efficient localized manner. These repairs preserve the edge expansion, spectral gap, and network stretch, after adversarial deletions, without increasing node degrees by too much, in the following sense. At any point in the algorithm, the expansion of the graph will be either ‘better’ than the expansion of the graph formed by considering only the adversarial insertions (not the adversarial deletions) or the expansion will be, at least, a constant. Also, the stretch i.e. the distance between any pair of nodes in the healed graph is no more than a O(log n) factor. Similarly, at any point, a node v whose degree would have been d in the graph with adversarial insertions only, will have degree at most O(κd) in the actual graph, for a small
Local Algorithms for Robust Mission Realization in LargeScale Disconnected Networks
"... Abstract—Robustness and reliability in largescale sensor networks present a triple challenge. First, the large scale (possibly compounded by the use of unreliable communication) makes failures—transient and permanent—unavoidable. Second, the difficulty of deploying such systems, especially in chal ..."
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Abstract—Robustness and reliability in largescale sensor networks present a triple challenge. First, the large scale (possibly compounded by the use of unreliable communication) makes failures—transient and permanent—unavoidable. Second, the difficulty of deploying such systems, especially in challenging environments, makes it next to impossible to replace failed nodes or rearrange surviving nodes after each failure. Third, the limited resources and capabilities of most sensor nodes prohibit elaborate, global/centralized approaches, especially in view of the potentially large number of nodes in the network. In this paper, we propose simple mechanisms to alleviate the effect of node failure in largescale disconnected networks and show that beneficial global network properties can emerge from local rules achievable by simple nodes. I. CONTEXT AND PROBLEM DEFINITION
Chapter 8 Analysis of PeertoPeer Botnet Attacks and Defenses
"... Abstract A “botnet ” is a network of computers that are compromised and controlled by an attacker (botmaster). Botnets are one of the most serious threats to today’s Internet. Most current botnets have centralized command and control (C&C) architecture. However, peertopeer (P2P) structured bo ..."
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Abstract A “botnet ” is a network of computers that are compromised and controlled by an attacker (botmaster). Botnets are one of the most serious threats to today’s Internet. Most current botnets have centralized command and control (C&C) architecture. However, peertopeer (P2P) structured botnets have gradually emerged as a new advanced form of botnets. Due to the distributive nature of P2P networks, P2P botnets are more resilient to defense countermeasures. In this chapter, first we systematically study P2P botnets along multiple dimensions: bot candidate selection, network construction, C&C communication mechanisms/protocols, and mitigation approaches. Then we provide mathematical analysis of two P2P botnet elimination approaches – index poisoning defense and Sybil defense, and one P2P botnet monitoring technique – passive monitoring based on infiltrated honeypots or captured bots. Simulation experiments show that our mathematical analysis is accurate.