Transcript Bayesian Belief Networks in Anomaly Detection, Fault Diagnosis

Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Bayesian Belief Networks in
Anomaly Detection, Fault
Diagnosis & Failure Prognostics
Joseph Gehring
Anthony Hadding
Santiago Salazar
Mentor: Dr. Janusz Zalewski
FGCU, April 2012
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
What is Bayesian Belief Network?
• Bayesian Belief Network is a graphical
method of data analysis employing an
algorithm based on the Bayes Theorem.
• It allows reasoning about data by deriving
conclusions about causes of system
behavior based on its symptoms.
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Bayesian Belief Networks
• Use data for known occurrences to determine
probabilities of unknown conditions.
• These relationships can be created in a
network, with each relationship building on
the probabilities known before.
• Software tools help represent complex
relationships in a simple, easy-to-use format
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Project Description
Use Bayesian Belief Networks to analyze data
from three different sources:
• Wireshark – to reason about network security
based on anomaly detection
• Solar Power Plant – to diagnose device faults
• NASA Engine Degradation – to predict failures
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Wireshark Data Set
• Network packet information
• Analyzed for threats and anomalies
Malformed packets
Unknown source
Broadcast destination
Combinations of these
• Other packet information also analyzed
How protocol influences packet length
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Wireshark Data Set
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Solar Plant Data Set
• Analyzed a month worth of solar plant sensor
data, collected every 15 minutes
• Temperature, Wind Speed, Voltage, Current,
Power, Solar Energy
• Analyzed for sensor faults and failures
Failed temperature sensor
Failed wind speed sensor
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
Solar Plant Data Set
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
NASA Data Set
• Turbofan Engine
Degradation Simulation Data
• 21 sensors, plus other values
• 100 different engines in the same network
• Failure prediction, using sensor input to
determine Remaining Useful Life based
on previously determined average life
Computer Science, Software Engineering & Robotics Workshop, FGCU, April 27-28, 2012
NASA Data Set