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A Decentralized Bayesian Attack Detection Algorithm for Network Security
 

Summary: A Decentralized Bayesian Attack Detection
Algorithm for Network Security
Kien C. Nguyen, Tansu Alpcan, and Tamer Bas¸ar
Abstract Decentralized detection has been an active area of research since the late
1970s. Its earlier application area has been distributed radar systems, and more re-
cently it has found applications in sensor networks and intrusion detection. The
most popular decentralized detection network structure is the parallel configuration,
where a number of sensors are directly connected to a fusion center. The sensors
receive measurements related to an event and then send summaries of their obser-
vations to the fusion center. Previous work has focused on separate optimization of
the quantization rules at the sensors and the fusion rule at the fusion center or on
asymptotic results when the number of sensors is very large and the observations
are conditionally independent and identically distributed given each hypothesis.
In this work, we examine the application of decentralized detection to intrusion
detection with again the parallel configuration, but with joint optimization. Particu-
larly, using the Bayesian approach, we seek a joint optimization of the quantization
rules at the sensors and the fusion rule at the fusion center. The observations of the
sensors are not assumed to be conditionally independent nor identically distributed.
We consider the discrete case where the distributions of the observations are given
as probability mass functions. We propose a search algorithm for the optimal so-

  

Source: Alpcan, Tansu - Deutsche Telekom Laboratories & Technische Universität Berlin

 

Collections: Engineering