12,081 research outputs found
The web of federal crimes in Brazil: topology, weaknesses, and control
Law enforcement and intelligence agencies worldwide struggle to find
effective ways to fight and control organized crime. However, illegal networks
operate outside the law and much of the data collected is classified.
Therefore, little is known about criminal networks structure, topological
weaknesses, and control. In this contribution we present a unique criminal
network of federal crimes in Brazil. We study its structure, its response to
different attack strategies, and its controllability. Surprisingly, the network
composed of multiple crimes of federal jurisdiction has a giant component,
enclosing more than a half of all its edges. This component shows some typical
social network characteristics, such as small-worldness and high clustering
coefficient, however it is much "darker" than common social networks, having
low levels of edge density and network efficiency. On the other side, it has a
very high modularity value, . Comparing multiple attack strategies, we
show that it is possible to disrupt the giant component of the network by
removing only of its edges or nodes, according to a module-based
prescription, precisely due to its high modularity. Finally, we show that the
component is controllable, in the sense of the exact network control theory, by
getting access to of the driver nodes.Comment: 9 pages, 5 figure
Effectiveness of dismantling strategies on moderated vs. unmoderated online social platforms
Online social networks are the perfect test bed to better understand
large-scale human behavior in interacting contexts. Although they are broadly
used and studied, little is known about how their terms of service and posting
rules affect the way users interact and information spreads. Acknowledging the
relation between network connectivity and functionality, we compare the
robustness of two different online social platforms, Twitter and Gab, with
respect to dismantling strategies based on the recursive censor of users
characterized by social prominence (degree) or intensity of inflammatory
content (sentiment). We find that the moderated (Twitter) vs unmoderated (Gab)
character of the network is not a discriminating factor for intervention
effectiveness. We find, however, that more complex strategies based upon the
combination of topological and content features may be effective for network
dismantling. Our results provide useful indications to design better strategies
for countervailing the production and dissemination of anti-social content in
online social platforms
Markov Decision Processes with Applications in Wireless Sensor Networks: A Survey
Wireless sensor networks (WSNs) consist of autonomous and resource-limited
devices. The devices cooperate to monitor one or more physical phenomena within
an area of interest. WSNs operate as stochastic systems because of randomness
in the monitored environments. For long service time and low maintenance cost,
WSNs require adaptive and robust methods to address data exchange, topology
formulation, resource and power optimization, sensing coverage and object
detection, and security challenges. In these problems, sensor nodes are to make
optimized decisions from a set of accessible strategies to achieve design
goals. This survey reviews numerous applications of the Markov decision process
(MDP) framework, a powerful decision-making tool to develop adaptive algorithms
and protocols for WSNs. Furthermore, various solution methods are discussed and
compared to serve as a guide for using MDPs in WSNs
Semi-Informed Multi-Agent Patrol Strategies
The adversarial multi-agent patrol problem is an active research topic with many real-world applications such as physical robots guarding an area and software agents protecting a computer network. In it, agents patrol a graph looking for so-called critical vertices that are subject to attack by adversaries. The agents are unaware of which vertices are subject to attack by adversaries and when they encounter such a vertex they attempt to protect it from being compromised (an adversary must occupy the vertex it targets a certain amount of time for the attack to succeed). Even though the terms adversary and attack are used, the problem domain extends to patrolling a graph for other interesting noncompetitive contexts such as search and rescue. The problem statement adopted in this work is formulated such that agents obtain knowledge of local graph topology and critical vertices over the course of their travels via an API ; there is no global knowledge of the graph or communication between agents. The challenge is to balance exploration, necessary to discover critical vertices, with exploitation, necessary to protect critical vertices from attack. Four types of adversaries were used for experiments, three from previous research – waiting, random, and statistical - and the fourth, a hybrid of those three. Agent strategies for countering each of these adversaries are designed and evaluated. Benchmark graphs and parameter settings from related research will be employed. The proposed research culminates in the design and evaluation of agents to counter these various types of adversaries under a range of conditions. The results of this work are agent strategies in which each agent becomes solely responsible for protecting those critical vertices it discovers. The agents use emergent behavior to minimize successful attacks and maximize the discovery of new critical vertices. A set of seven edge choosing primitives (ECPs) are defined that are combined in different ways to yield a range of agent strategies using the chain of responsibility OOP design pattern. Every permutation of them were tested and measured in order to identify those strategies that perform well. One strategy performed particularly well against all adversaries, graph topology, and other experimental variables. This particular strategy combines ECPs of: A hard-deadline return to covered vertices to counter the random adversary, efficiently checking vertices to see if they are being attacked by the waiting adversary, and random movement to impede the statistical adversary
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