325 research outputs found
Deep reinforcement learning for attacking wireless sensor networks
Recent advances in Deep Reinforcement Learning allow solving increasingly complex problems. In this work, we show how current defense mechanisms in Wireless Sensor Networks are vulnerable to attacks that use these advances. We use a Deep Reinforcement Learning attacker architecture that allows having one or more attacking agents that can learn to attack using only partial observations. Then, we subject our architecture to a test-bench consisting of two defense mechanisms against a distributed spectrum sensing attack and a backoff attack. Our simulations show that our attacker learns to exploit these systems without having a priori information about the defense mechanism used nor its concrete parameters. Since our attacker requires minimal hyper-parameter tuning, scales with the number of attackers, and learns only by interacting with the defense mechanism, it poses a significant threat to current defense procedures
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
Approximate information state based convergence analysis of recurrent Q-learning
In spite of the large literature on reinforcement learning (RL) algorithms
for partially observable Markov decision processes (POMDPs), a complete
theoretical understanding is still lacking. In a partially observable setting,
the history of data available to the agent increases over time so most
practical algorithms either truncate the history to a finite window or compress
it using a recurrent neural network leading to an agent state that is
non-Markovian. In this paper, it is shown that in spite of the lack of the
Markov property, recurrent Q-learning (RQL) converges in the tabular setting.
Moreover, it is shown that the quality of the converged limit depends on the
quality of the representation which is quantified in terms of what is known as
an approximate information state (AIS). Based on this characterization of the
approximation error, a variant of RQL with AIS losses is presented. This
variant performs better than a strong baseline for RQL that does not use AIS
losses. It is demonstrated that there is a strong correlation between the
performance of RQL over time and the loss associated with the AIS
representation.Comment: 25 pages, 6 figure
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