20 research outputs found
Coordination in Network Security Games: a Monotone Comparative Statics Approach
Malicious softwares or malwares for short have become a major security
threat. While originating in criminal behavior, their impact are also
influenced by the decisions of legitimate end users. Getting agents in the
Internet, and in networks in general, to invest in and deploy security features
and protocols is a challenge, in particular because of economic reasons arising
from the presence of network externalities.
In this paper, we focus on the question of incentive alignment for agents of
a large network towards a better security. We start with an economic model for
a single agent, that determines the optimal amount to invest in protection. The
model takes into account the vulnerability of the agent to a security breach
and the potential loss if a security breach occurs. We derive conditions on the
quality of the protection to ensure that the optimal amount spent on security
is an increasing function of the agent's vulnerability and potential loss. We
also show that for a large class of risks, only a small fraction of the
expected loss should be invested.
Building on these results, we study a network of interconnected agents
subject to epidemic risks. We derive conditions to ensure that the incentives
of all agents are aligned towards a better security. When agents are strategic,
we show that security investments are always socially inefficient due to the
network externalities. Moreover alignment of incentives typically implies a
coordination problem, leading to an equilibrium with a very high price of
anarchy.Comment: 10 pages, to appear in IEEE JSA
Observer Placement for Source Localization: The Effect of Budgets and Transmission Variance
When an epidemic spreads in a network, a key question is where was its
source, i.e., the node that started the epidemic. If we know the time at which
various nodes were infected, we can attempt to use this information in order to
identify the source. However, maintaining observer nodes that can provide their
infection time may be costly, and we may have a budget on the number of
observer nodes we can maintain. Moreover, some nodes are more informative than
others due to their location in the network. Hence, a pertinent question
arises: Which nodes should we select as observers in order to maximize the
probability that we can accurately identify the source? Inspired by the simple
setting in which the node-to-node delays in the transmission of the epidemic
are deterministic, we develop a principled approach for addressing the problem
even when transmission delays are random. We show that the optimal
observer-placement differs depending on the variance of the transmission delays
and propose approaches in both low- and high-variance settings. We validate our
methods by comparing them against state-of-the-art observer-placements and show
that, in both settings, our approach identifies the source with higher
accuracy.Comment: Accepted for presentation at the 54th Annual Allerton Conference on
Communication, Control, and Computin
Epidemic Spreading with External Agents
We study epidemic spreading processes in large networks, when the spread is
assisted by a small number of external agents: infection sources with bounded
spreading power, but whose movement is unrestricted vis-\`a-vis the underlying
network topology. For networks which are `spatially constrained', we show that
the spread of infection can be significantly speeded up even by a few such
external agents infecting randomly. Moreover, for general networks, we derive
upper-bounds on the order of the spreading time achieved by certain simple
(random/greedy) external-spreading policies. Conversely, for certain common
classes of networks such as line graphs, grids and random geometric graphs, we
also derive lower bounds on the order of the spreading time over all
(potentially network-state aware and adversarial) external-spreading policies;
these adversarial lower bounds match (up to logarithmic factors) the spreading
time achieved by an external agent with a random spreading policy. This
demonstrates that random, state-oblivious infection-spreading by an external
agent is in fact order-wise optimal for spreading in such spatially constrained
networks
The Behavior of Epidemics under Bounded Susceptibility
We investigate the sensitivity of epidemic behavior to a bounded
susceptibility constraint -- susceptible nodes are infected by their neighbors
via the regular SI/SIS dynamics, but subject to a cap on the infection rate.
Such a constraint is motivated by modern social networks, wherein messages are
broadcast to all neighbors, but attention spans are limited. Bounded
susceptibility also arises in distributed computing applications with download
bandwidth constraints, and in human epidemics under quarantine policies.
Network epidemics have been extensively studied in literature; prior work
characterizes the graph structures required to ensure fast spreading under the
SI dynamics, and long lifetime under the SIS dynamics. In particular, these
conditions turn out to be meaningful for two classes of networks of practical
relevance -- dense, uniform (i.e., clique-like) graphs, and sparse, structured
(i.e., star-like) graphs. We show that bounded susceptibility has a surprising
impact on epidemic behavior in these graph families. For the SI dynamics,
bounded susceptibility has no effect on star-like networks, but dramatically
alters the spreading time in clique-like networks. In contrast, for the SIS
dynamics, clique-like networks are unaffected, but star-like networks exhibit a
sharp change in extinction times under bounded susceptibility.
Our findings are useful for the design of disease-resistant networks and
infrastructure networks. More generally, they show that results for existing
epidemic models are sensitive to modeling assumptions in non-intuitive ways,
and suggest caution in directly using these as guidelines for real systems
Diffusion and Cascading Behavior in Random Networks
The spread of new ideas, behaviors or technologies has been extensively
studied using epidemic models. Here we consider a model of diffusion where the
individuals' behavior is the result of a strategic choice. We study a simple
coordination game with binary choice and give a condition for a new action to
become widespread in a random network. We also analyze the possible equilibria
of this game and identify conditions for the coexistence of both strategies in
large connected sets. Finally we look at how can firms use social networks to
promote their goals with limited information. Our results differ strongly from
the one derived with epidemic models and show that connectivity plays an
ambiguous role: while it allows the diffusion to spread, when the network is
highly connected, the diffusion is also limited by high-degree nodes which are
very stable
Back to the Source: an Online Approach for Sensor Placement and Source Localization
Source localization, the act of finding the originator of a disease or rumor in a network, has become an important problem in sociology and epidemiology. The localization is done using the infection state and time of infection of a few designated sensor nodes; however, maintaining sensors can be very costly in practice. We propose the first online approach to source localization: We deploy a priori only a small number of sensors (which reveal if they are reached by an infection) and then iteratively choose the best location to place new sensors in order to localize the source. This approach allows for source localization with a very small number of sensors; moreover, the source can be found while the epidemic is still ongoing. Our method applies to a general network topology and performs well even with random transmission delays
Topics in random graphs, combinatorial optimization, and statistical inference
The manuscript is made of three chapters presenting three differenttopics on which I worked with Ph.D. students. Each chapter can be read independently of the others andshould be relatively self-contained. Chapter 1 is a gentle introduction to the theory of random graphswith an emphasis on contagions on such networks. In Chapter 2, I explain the main ideas of the objectivemethod developed by Aldous and Steele applied to the spectral measure of random graphs and themonomer-dimer problem. This topic is dear to me and I hope that this chapter will convince the readerthat it is an exciting field of research. Chapter 3 deals with problems in high-dimensional statistics whichnow occupy a large proportion of my time. Unlike Chapters 1 and 2 which could be easily extended inlecture notes, I felt that the material in Chapter 3 was not ready for such a treatment. This field ofresearch is currently very active and I decided to present two of my recent contributions