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Control-Theoretic Approaches for Dynamic Information Assurance

Control-Theoretic Approaches for Dynamic Information Assurance. George Vachtsevanos Georgia Tech Working Meeting U. C. Berkeley February 5, 2003. The Information Assurance – Software Architecture Connection. Dynamic information assurance will require models of computation that

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Control-Theoretic Approaches for Dynamic Information Assurance

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  1. 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 client SYSTEM CONTROLLER/ DIAGNOSTICIAN Q S Controller NETWORK OF SENSORS feedback Q◦S Controller :

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

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

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

  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

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