Control-Theoretic Approaches for Dynamic Information Assurance George Vachtsevanos Georgia Tech Working Meeting U. C. Berkeley February 5, 2003

2 The Information Assurance – Software Architecture Connection  Dynamic information assurance will require models of computation that  Can direct the behavior of intelligent controller components, route/re-route and blend signals  Specify and validate strategies that involve real-time Q◦S parameters and fault-tolerant constraints  timed multitasking domains  Can support reconfiguration strategies involving transient compensation (control) and dynamic transitions

3  Can monitor status of configuration changes and globally coordinate them  Can handle unexpected conditions (large-grain disturbances, pop-up targets, etc.) that may arise during a transition; interrupt and safely back-out of a transition *”Smart” models of computation are required to support concepts and models of information assurance.

4 Q◦S Controller Communicates with sensor client, i.e. system controller, diagnostic routines, system status, etc. Measures on-line available bandwidth and other performance measures and executes Q◦S algorithm Q S Controller NETWORK OF SENSORS SYSTEM CONTROLLER/ DIAGNOSTICIAN feedback client Q◦S Controller :

5 Dynamic Q◦S Control N i (q) - bandwidth required by application (constraints) N imax (t) - available bandwidth at time t q(t) - vector of sampling rates, bits-per-pixel, etc. - sensor control F(q) - user satisfaction function ADAPTIVE NEURAL NET F (q) S (Q) S(N imax -N i (q)) N i (q) F min + - N imax (t)

6 CONTENTION FOR SHARED RESOURCES REAL-TIME RESPONSIVENESS DEPENDABILITY PRECISION QUALITY OF RESULTS MODIFY / RECONFIGURE / RESCHEDULE RESOURCES  BANDWIDTH  DYNAMIC SCHEDULING  FAULT TOLERANCE  RECONFIGURATION  OTHERS CRITICAL APPLICATIONS Q◦S MECHANISM FAILURES DYNAMIC WORKLOADS PERFORMANCE ASSESSMENT IDENTIFY / PREDICT DISTURBANCES

7 Sensors 101 Raw data  Information  Knowledge  What kind of data?  What type of sensors?  How many?  Where do we place them? NSF/Other supported activities

8 On the Concept of “Fusion”  Sensor Fusion –Data Fusion –Feature Fusion –Sensor Fusion –Report Fusion  Knowledge Fusion

9 Sensor Fusion (or Integration) Objective: Optimize performance of information gathering process Intelligent sensor and knowledge fusion algorithms based on focus of attention via active perception and Dempster-Shafer theory Sensor integration at various levels of abstraction - the data, feature, sensor and report levels Distinguishability and effectiveness measures defined to guide the sensor integration task Off-line and on-line learning techniques for effective data combination

10 Optimum Sensor Placement Strategies Traditional vs. proposed procedure Model Figure-of-Merit Selection Optimization Fig. 2a: Traditional sensor placement procedure. Model Figure-of-Merit Selection Optimization Fig. 2b: Proposed sensor placement procedure. Performance Assessment FMECA

11 The Value of Information Question: How do we assess the value of information? How do we maximize it?  Metrics  Optimization techniques  Control-theoretic concepts Examples from diagnosis/prognosis, control, alarming, etc.

12 Active Diagnosis Extends the offline ideas of “Probing” or “Testing” It is biased to monitor normal conditions Active Diagnosis Monitors consistency among data Active Diagnosis of DES - A Design Time Approach –the system itself is not diagnosable –design a controller called “Diagnostic Controller” that will make the system diagnosable Active Diagnosis Possibilities: –Inline with Intelligent Agent paradigm –Collaboration in Multiagent Systems can be directed to achieve Active Diagnosis

13 Active vs. Passive Diagnosis Passive Diagnosis:  Diagnoser FSM that monitors events and sensors to generate diagnosis.  A Diagnosable Plant generates a language from which unobservable failure conditions can be uniquely inferred by the Diagnoser FSM. Design-Time Active Diagnosis:  Design a controller that will make an otherwise “non-diagnosable” plant generate a language that is diagnosable.

14 Active Diagnosis - Agent Perspective Given an anomalous situation, Diagnostic Agent Plans, Learns, and Coordinates. –Learning takes place between distributed agents that share their experiences –Coordination helps search, retrieval, adapting activities –Planning is required to determine if learning and coordination is possible in the given expected time-to-failure condition “Run-time” Active Diagnosis –non-intrusive –autonomous and rational

15 Information Assurance Enabling Technologies:  Sensor Fusion  Data Validation  Q◦S methods  Performance metrics