23,088 research outputs found
A model of human event detection in multiple process monitoring situations
It is proposed that human decision making in many multi-task situations might be modeled in terms of the manner in which the human detects events related to his tasks and the manner in which he allocates his attention among his tasks once he feels events have occurred. A model of human event detection performance in such a situation is presented. An assumption of the model is that, in attempting to detect events, the human generates the probability that events have occurred. Discriminant analysis is used to model the human's generation of these probabilities. An experimental study of human event detection performance in a multiple process monitoring situation is described and the application of the event detection model to this situation is addressed. The experimental study employed a situation in which subjects simulataneously monitored several dynamic processes for the occurrence of events and made yes/no decisions on the presence of events in each process. Input to the event detection model of the information displayed to the experimental subjects allows comparison of the model's performance with the performance of the subjects
An Evasion and Counter-Evasion Study in Malicious Websites Detection
Malicious websites are a major cyber attack vector, and effective detection
of them is an important cyber defense task. The main defense paradigm in this
regard is that the defender uses some kind of machine learning algorithms to
train a detection model, which is then used to classify websites in question.
Unlike other settings, the following issue is inherent to the problem of
malicious websites detection: the attacker essentially has access to the same
data that the defender uses to train its detection models. This 'symmetry' can
be exploited by the attacker, at least in principle, to evade the defender's
detection models. In this paper, we present a framework for characterizing the
evasion and counter-evasion interactions between the attacker and the defender,
where the attacker attempts to evade the defender's detection models by taking
advantage of this symmetry. Within this framework, we show that an adaptive
attacker can make malicious websites evade powerful detection models, but
proactive training can be an effective counter-evasion defense mechanism. The
framework is geared toward the popular detection model of decision tree, but
can be adapted to accommodate other classifiers
Detection Mechanism in SNSPD: Numerical Results of a Conceptually Simple, Yet Powerful Detection Model
In a recent publication we have proposed a numerical model that describes the
detection process of optical photons in superconducting nanowire single-photon
detectors (SNSPD). Here, we review this model and present a significant
improvement that allows us to calculate more accurate current distributions for
the inhomogeneous quasi-particle densities occurring after photon absorption.
With this new algorithm we explore the detector response in standard NbN SNSPD
for photons absorbed off-center and for 2-photon processes. We also discuss the
outstanding performance of SNSPD based on WSi. Our numerical results indicate a
different detection mechanism in WSi than in NbN or similar materials.Comment: Presented at ASC 2014 (invited) and submitted to IEEE Transaction on
Applied Superconductivity (Special Issue
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